social media sentiment analysis research paper

An Overview of Social Media and Sentiment Analysis

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• Americans’ Changing Relationship With Local News

As news consumption habits become more digital, U.S. adults continue to see value in local outlets

Table of contents.

• 1. Attention to local news
• 2. Local news topics
• Americans’ changing local news providers
• How people feel about their local news media’s performance
• Most Americans think local journalists are in touch with their communities
• Interactions with local journalists
• 5. Americans’ views on the financial health of local news
• Acknowledgments
• The American Trends Panel survey methodology

The Pew-Knight Initiative supports new research on how Americans absorb civic information, form beliefs and identities, and engage in their communities.

Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. Knight Foundation is a social investor committed to supporting informed and engaged communities. Learn more >

Pew Research Center conducted this study to better understand the local news habits and attitudes of U.S. adults. It is a follow-up to a similar study conducted in 2018 .

The survey of 5,146 U.S. adults was conducted from Jan. 22 to 28, 2024. Everyone who completed the survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories.  Read more about the ATP’s methodology .

Refer to the topline for the questions used for this survey , along with responses, and to the methodology for more details.

This is a Pew Research Center report from the Pew-Knight Initiative, a research program funded jointly by The Pew Charitable Trusts and the John S. and James L. Knight Foundation. Find related reports online at https://www.pewresearch.org/pew-knight/ .

The local news landscape in America is going through profound changes as both news consumers and producers continue to adapt to a more digital news environment. We recently asked U.S. adults about the ways they access local news, as well as their attitudes toward local journalism, finding that:

• A growing share of Americans prefer to get local news online, while fewer are getting news on TV or in print. And newspapers are no longer primarily consumed as a print product – the majority of readers of local daily newspapers now access them digitally.
• The share of U.S. adults who say they are paying close attention to local news has dropped since our last major survey of attitudes toward local news in 2018, mirroring declining attention to national news.
• Americans still see value in local news and local journalists. A large majority say local news outlets are at least somewhat important to the well-being of their local community. Most people also say local journalists are in touch with their communities and that their local news media perform well at several aspects of their jobs, such as reporting the news accurately.
• At the same time, a relatively small share of Americans (15%) say they have paid for local news in the last year. And many seem unaware of the major financial challenges facing local news: A 63% majority (albeit a smaller majority than in 2018) say they think their local news outlets are doing very or somewhat well financially.
• Majorities of both major parties say local media in their area are doing their jobs well. While Republicans and GOP-leaning independents are slightly less positive than Democrats and Democratic leaners in their opinions of local media, views of local news don’t have the same stark political divides that exist within Americans’ opinions about national media .
• Most Americans say local journalists should remain neutral on issues in their community, but a substantial minority say local journalists should take a more active role. About three-in-ten say local journalists should advocate for change in their communities, a view that’s especially common among Democrats and younger adults.

These are some of the key findings from a new Pew Research Center survey of about 5,000 U.S. adults conducted in January 2024. This is the first in a series of Pew Research Center reports on local news from the Pew-Knight Initiative, a research program funded jointly by The Pew Charitable Trusts and the John S. and James L. Knight Foundation.

Americans largely hold positive views of local news organizations

At a time when many local news outlets are struggling and Americans’ trust in the news media has waned, the vast majority of U.S. adults (85%) say local news outlets are at least somewhat important to the well-being of their local community. This includes 44% who say local journalism is extremely or very important to their community

About seven-in-ten U.S. adults (69%) say that local journalists in their area are mostly in touch with their community, up from 63% who said this in 2018. And most Americans also say their local news organizations are doing well at four key roles:

• Reporting news accurately (71%)
• Covering the most important stories (68%)
• Being transparent (63%)
• Keeping an eye on local political leaders (61%).

These are relatively positive views compared with how Americans see news organizations more broadly. For instance, a 2022 Pew Research Center survey found that fewer than half of U.S. adults say that news organizations in general do a very or somewhat good job of covering the most important stories, reporting the news accurately and serving as a watchdog over elected leaders.

What’s more, views toward local news are not as politically polarized as Americans’ opinions about the news media overall. While Republicans and GOP-leaning independents are not quite as positive as Democrats and Democratic leaners in some of their assessments of local journalists, most Republicans still say the local media in their area are doing their jobs well.

For example, roughly three-quarters of Democrats (78%) say their local media do well at reporting news accurately, compared with about two-thirds of Republicans (66%).

By comparison, the 2022 survey found that 51% of Democrats and just 17% of Republicans say that news organizations in general do a very or somewhat good job of reporting the news accurately.

Jump to more information on views toward local news organizations.

Fewer Americans are closely following local news – and other types of news

Despite these positive views toward local news organizations, there are signs that Americans are engaging less with local journalism than they used to.

The share of Americans who say they follow local news very closely has fallen by 15 percentage points since 2016 (from 37% to 22%). Most U.S. adults still say they follow local news at least somewhat closely (66%), but this figure also has dropped in recent years.

This trend is not unique to local news – Americans’ attention to national and international news also has declined.

The local news landscape is becoming more digital

The ways in which Americans access local news are changing, reflecting an increasingly digital landscape – and matching patterns in overall news consumption habits .

Preferred pathways to local news

• Fewer people now say they prefer to get local news through a television set (32%, down from 41% who said the same in 2018).
• Americans are now more likely to say they prefer to get local news online, either through news websites (26%) or social media (23%). Both of these numbers have increased in recent years.
• Smaller shares prefer getting their local news from a print newspaper or on the radio (9% each).

Specific sources for local news

The types of sources (e.g., outlets or organizations) Americans are turning to are changing as well:

• While local television stations are still the most common source of local news beyond friends, family and neighbors, the share who often or sometimes get news there has declined from 70% to 64% in recent years.
• Online forums, such as Facebook groups or the Nextdoor app, have become a more common destination for local news: 52% of U.S. adults say they at least sometimes get local news from these types of forums, up 14 percentage points from 2018. This is on par with the percentage who get local news at least sometimes from local radio stations.
• Meanwhile, a third of Americans say they at least sometimes get local news from a daily newspaper, regardless of whether it is accessed via print, online or through a social media website – down 10 points from 2018. The share of Americans who get local news from newspapers is now roughly on par with the share who get local news from local government agencies (35%) or local newsletters or Listservs (31%).

Not only are fewer Americans getting local news from newspapers, but local daily newspapers are now more likely to be accessed online than in print.

• 31% of those who get news from daily newspapers do so via print, while far more (66%) do so digitally, whether through websites, apps, emails or social media posts that include content from the paper.
• In 2018, just over half of those who got news from local daily newspapers (54%) did so from print, and 43% did so via a website, app, email or social media site.

There is a similar move toward digital access for local TV stations, though local TV news is still mostly consumed through a TV set.

• In 2024, 62% of those getting news from local TV stations do so through a television, compared with 37% who do so through one of the digital pathways.
• An even bigger majority of local TV news consumers (76%) got that news through a TV set in 2018.

Jump to more information on how people access local news.

The financial state of local news

The turmoil for the local news industry in recent years has come with major financial challenges. Circulation and advertising revenue for newspapers have seen sharp declines in the last decade, according to our analysis of industry data , and other researchers have documented that thousands of newspapers have stopped publishing in the last two decades. There also is evidence of audience decline for local TV news stations, although advertising revenue on local TV has been more stable.

When asked about the financial state of the news outlets in their community, a majority of Americans (63%) say they think their local news outlets are doing very or somewhat well, with a third saying that they’re not doing too well or not doing well at all. This is a slightly more pessimistic view than in 2018, when 71% said their local outlets were doing well, though it is still a relatively positive assessment of the financial state of the industry.

Just 15% of Americans say they have paid or given money to any local news source in the past year – a number that has not changed much since 2018. The survey also asked Americans who did not pay for news in the past year the main reason why not. The most common explanation is that people don’t pay because they can find plenty of free local news, although young adults are more inclined to say they just aren’t interested enough in local news to pay for it.

Jump to more information on how people view the financial state of local news.

Other key findings in this report

Americans get local news about a wide variety of topics. Two-thirds or more of U.S. adults at least sometimes get news about local weather, crime, government and politics, and traffic and transportation, while smaller shares (but still at least half) say they get local news about arts and culture, the economy, schools, and sports.

Relatively few Americans are highly satisfied with the coverage they see of many topics. The survey also asked respondents who at least sometimes get each type of local news how satisfied they are with the news they get. With the exception of weather, fewer than half say they are extremely or very satisfied with the quality of the news they get about each topic. For example, about a quarter of those who consume news about their local economy (26%) say they are extremely or very satisfied with this news. Read more about different local news topics in Chapter 2.

When asked whether local journalists should remain neutral on community issues or advocate for change in the community, a majority of Americans (69%) say journalists should remain neutral, reflecting more traditional journalistic norms. However, 29% say that local journalists should be advocating for change in their communities. Younger adults are the most likely to favor advocacy by journalists: 39% of those ages 18 to 29 say that local journalists should push for change, as do 34% of those 30 to 49. Read more about Americans’ views of the role of local journalists in Chapter 4.

Americans who feel a strong sense of connection to their community are more likely to engage with local news, say that local news outlets are important to the community, and rate local media more highly overall. For example, 66% of those who say they are very attached to their community say local news outlets are extremely or very important to the well-being of their local community, compared with 46% of those who are somewhat attached and 31% of those who are not very or not at all attached to their community.

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Here’s why so many Republicans won’t buy EVs

Democrats say they are way more likely than Republicans to buy electric cars. Could that change?

Electric cars have taken off across the United States. Even amid news of slowing sales , the country sold almost 1.2 million fully electric vehicles in 2023, more than quadruple the number in 2019. Grocery stores and rest stops are installing charging stations across the country; electric cars have moved beyond niche status and are being produced by Ford, GM, Hyundai and many others.

But there is one thing holding the nation back from the dream of an all-electric future: political polarization. Sales data have consistently shown that while Democrats have been buying the new cars in droves, Republicans haven’t jumped onto the EV-buying train.

“The Republican is like, ‘They’re trying to ban gas cars — I’m not going to buy a Biden-mobile,’” said Mike Murphy, a former Republican strategist who runs the nonprofit EV Politics Project, which attempts to counter misinformation on electric cars and encourage conservatives to adopt the vehicles.

Personal cars account for 20 percent of U.S. planet-warming emissions, and more Americans still prefer gas-powered ones. A Washington Post-University of Maryland poll last year found that 46 percent of respondents favored a gas car, compared with 19 percent who wanted a fully electric vehicle. If that doesn’t change, it will be almost impossible for the United States to meet its climate goals.

According to a Gallup poll conducted in March of this year, 61 percent of Democrats reported that they were “seriously considering” or “might consider” buying an EV in the future — compared with only 24 percent of Republicans. At the same time, 69 percent of Republicans said that they “would not buy” an EV in future, compared with 27 percent of Democrats. The difference in Democratic and Republican respondents who owned an EV was within the margin of error.

Actual sales show a partisan trend. According to an analysis from researchers at the University of California at Berkeley, MIT and HEC Montréal, between 2012 and 2022, about half of all EVs sold went to the top 10 percent most Democratic counties in the United States, and about a third of all EVs sold went to the top 5 percent most Democratic counties. That pattern persisted when researchers analyzed the most Democratic states, according to the working paper, which has not yet been peer-reviewed.

The finding held when researchers accounted for income, gas prices and population density. That means that even when looking at dense, urban areas — which are likely to have more public EV charging — Democratic counties outweighed Republican ones in EV adoption.

“There’s an incredible correlation with political ideology,” said Lucas Davis, a professor of business and technology at the University of California at Berkeley and the lead author on the analysis.

During the 10 years of the study, Davis said, the electric vehicle market swelled from just a few all-electric models to dozens, with annual sales growing from around 50,000 EVs to 750,000. But the proportion of those vehicles going to Democratic counties stayed nearly constant. “It’s incredibly consistent across time,” Davis said.

There are other possible explanations, he said: For example, buyers in counties with a higher EV share could notice electric cars more, and ultimately consider buying one themselves.

Still, Democratic areas have much more than their fair share of electric cars. So why did electric cars become — and stay — so polarized?

One reason is that Republican leaders have injected electric vehicles into the culture wars, in light of President Biden’s effort to move the country away from gas cars. Former president Donald Trump has railed against electric cars, calling support of them “electric car lunacy” and the push for an EV future “very, very stupid.”

“I think it is getting more polarized,” Murphy said. “Republicans are instinctively: ‘If Biden’s for it, we’re against.’”

An analysis of social media content by The Post shows that conservative criticism of electric cars rose in August 2022 after California announced rules to phase out fully gas-powered cars by 2035 — and when the Biden administration finalized an Environmental Protection Agency rule in March that would push automakers to make more than 50 percent of cars sold by 2032 all-electric or hybrid.

Conservatives talking about EVs often make a few standard critiques: that switching to electric cars will end up supporting China, that the vehicles are too expensive for ordinary Americans, or that Democrats are taking away Americans’ right to choose which cars they drive.

“It’s a religion,” Tucker Carlson said on Fox News shortly after California announced its plan to phase out gas-powered cars. “It’s about making them feel like good people and increasing their control over you.”

But there are other reasons Republicans might be more reluctant to switch to electric cars. Marc Hetherington, a political science professor at the University of North Carolina at Chapel Hill and a co-author of the 2018 book “Prius or Pickup?” said that political polarization partly stems from deep-seated psychological “worldviews.” Liberals tend to be more afraid of systemic issues like climate change compared to conservatives. Conservatives also tend to be more hesitant to adopt new technologies; liberals are more open to change.

Those preferences can lead Republicans to stick with the large cars and trucks that they know; it can also lead liberals to opt for hybrids or electric vehicles.

When it comes to Democrats adopting EVs, Davis said, “It demonstrates your environmental bona fides to your neighbors.”

And once those habits are created, they become calcified. Cars become not just a way to get around but a form of personal expression, identity and group membership. Democrats see friends and family driving EVs and want to do the same; Republicans hear elite members of their party blasting electric cars and opt to stay with gas.

Some analysts hope to change how conservatives see electric cars. John Marshall, the founder and CEO of the Potential Energy Coalition , a group that uses marketing strategies to boost climate action, says that his group’s research shows that abstract messaging — around EVs as job creators or as a path to energy independence — doesn’t work as well as pointing out how EVs cut pollution and the cost of owning a car. (According to an analysis by the group Energy Innovation, EVs can save $14 to $80 per refuel.)

Talk of banning gas-powered cars also doesn’t help. “Mandates, bans and limitations never win,” Marshall said. “Whenever one of those three things is mentioned, you lose support pretty significantly.”

Murphy said carmakers need to focus primarily on how they can help consumers. “They’re fast, they’re quiet, need much less maintenance,” he said.

Gil Tal, the director of the Electric Vehicle Research Center at the University of California at Davis, says that resistance to new technologies is a normal part of the process. But he worries that this partisan divide could slow the process. “We need long-term investment and commitment in order to complete this push,” he said. “It’s not going to happen by itself.”

About 40 percent of new car buyers are Republicans, Murphy said — without them, the country won’t be able to shift fully to electric vehicles.

One of the big questions going forward is how much of the trend is due to deep-seated disagreement on electric cars — and how much is simply the effect of seeing friends and neighbors owning the vehicles. Multiple studies have shown that actions like buying solar panels or buying EVs are “contagious”; they spread among communities.

That means that if EVs gain a foothold in Republican areas, they could spread quickly.

“If you know someone who has one, you’re far more interested and pro-EV than not,” Murphy said, “because the real-life experience contradicts the myth.”

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Trump Media Stock Rises Again, Adding Billions to Former President’s Stake

In their first few weeks of trading, shares of Truth Social’s parent company soared, then crashed, and have recently made a strong recovery. Former President Donald J. Trump’s holding is now worth about $6 billion.

By Matthew Goldstein

Shares of former President Donald J. Trump’s social media company have been on a wild ride since making their debut on Wall Street in March: soaring, crashing and then climbing again.

The rally has pushed the value of Mr. Trump’s majority stake in the company to some $6 billion, a major windfall as he ramps up his presidential campaign and faces steep legal bills tied to the multiple cases against him.

On Wednesday, the share price of Trump Media & Technology Group closed around $53, approaching where the parent company of Truth Social ended its first day of trading on March 26, at just under $58.

Mr. Trump owns nearly two-thirds of the company, but he is not allowed to sell his shares or use them as collateral for a few months. Trump Media’s multibillion-dollar market valuation has put it in the same league as established companies like American Airlines and Hasbro.

The frenzied trading in Trump Media has been reminiscent of the mania around so-called meme stocks — shares that trade based on investor sentiment and momentum more than traditional financial fundamentals.

The company last year lost $58 million and took in just $4 million in revenue, all of it from advertising on Truth Social.

“A meme stock has no economic rationale,” said Mike Stegemoller, a finance professor at Baylor University. “It is like someone acting erratically on the street — just get out the way.”

In classic meme-stock fashion, there have been no major developments at Trump Media to propel the shares higher of late. But the stock trades in relation to how investors view Mr. Trump’s prospects in the upcoming presidential election, their fealty to the former president, and simply momentum and emotion.

The recent rise in Trump Media’s share price has also provided a sharp contrast to the scenes in a New York courtroom, where Mr. Trump is on trial for his role in a scheme to conceal hush-money payments to Stormy Daniels, an adult film star, in the final days of the 2016 presidential campaign.

Trump Media merged with a public shell company for its market listing, and more than 400,000 of the shareholders of that company are individual investors. Many of them are fans of Mr. Trump and users of Truth Social.

Digital World Acquisition Corporation, the shell company, began the year trading around $17.50 a share. That means it has been a banner year for early investors in Digital World who are now shareholders of Trump Media.

Michael Melkersen, a lawyer who lives in Puerto Rico, invested in Digital World before its initial public offering in 2021 and now has hundreds of thousands of shares of Trump Media. He said he had taken a gamble that paid off and was now looking to sell some shares.

“I believed in it,” he said, “but I am too heavily invested for my own comfort.”

Recent regulatory filings show that just a handful of investment advisory firms, which manage money for wealthy investors, held a meaningful amount of Trump Media shares at the end of March.

Sapient Capital, which manages $9 billion in assets for wealthy people and foundations, reported holding about 145,000 shares of Trump Media. A founding partner at the Indianpolis-based firm is a brother of Mike Pence, who served as vice president for Mr. Trump. The former vice president said in March that he would not endorse Mr. Trump this year. A Sapient spokesman said the shares were purchased at the request of its clients.

The filings also show that a few investment firms had significant positions in Trump Media warrants — a type of security that can be converted into shares in the future. The firms included Selway Asset Management, Magnus Financial Group and Nine Masts Capital, according to the regulatory tracking service Whalewisdom.

Justin Yochum, a co-owner of Selway, an Idaho-based investment advisory firm, said the firm had bought the warrants as part of a complex trading strategy to profit on the price difference between Trump Media’s shares and its warrants.

“I believe the company itself is worth zero,” Mr. Yochum said.

A few hedge funds reported holding “put” options on the stock. These allow investors to sell shares at a future date at a specified price, and are often bought by traders who think the price of a stock will fall.

A recent analysis of the ownership of Trump Media shares by S&P Global Market Intelligence found that Mr. Trump, corporate insiders and an entity closely affiliated with Trump Media owned nearly 96 percent of the social media company’s stock. Retail investors owned about 3.9 percent of Trump Media’s stock, or roughly 5.4 million shares, and institutional investors owned less than 1 percent.

Trump Media has been pushing back against short-sellers, as investors who bet on a stock’s fall are known, sending letters to stock exchanges and lawmakers asking for investigations into what it describes as “manipulation.”

S3 Partners, a market research firm, estimates that short sellers have incurred losses of $216 million on paper this year by betting against Trump Media.

As it happens, short-sellers have also recently been squeezed by a resurgence of GameStop, the original meme stock. Those losses were even greater, at more than $800 million, according to S3.

The stock roared back to life this week after the apparent return to social media of Keith Gill, a former broker who goes by the moniker “Roaring Kitty,” after a three-year hiatus. Mr. Gill was at the center of the initial meme-stock mania in 2021, when he encouraged investors to buy shares of GameStop, which he argued was undervalued.

GameStop’s stock has roughly doubled since Monday, on little news other than a series of cryptic posts on Mr. Gill’s X account.

Matthew Goldstein covers Wall Street and white-collar crime and housing issues. More about Matthew Goldstein

Survey on sentiment analysis: evolution of research methods and topics

• Published: 06 January 2023
• Volume 56 , pages 8469–8510, ( 2023 )

Cite this article

• Jingfeng Cui   ORCID: orcid.org/0000-0001-8306-0727 1 , 2 ,
• Zhaoxia Wang   ORCID: orcid.org/0000-0001-7674-5488 3 ,
• Seng-Beng Ho   ORCID: orcid.org/0000-0003-4839-1509 1 &
• Erik Cambria   ORCID: orcid.org/0000-0002-3030-1280 4  

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Sentiment analysis, one of the research hotspots in the natural language processing field, has attracted the attention of researchers, and research papers on the field are increasingly published. Many literature reviews on sentiment analysis involving techniques, methods, and applications have been produced using different survey methodologies and tools, but there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. There have also been few survey works leveraging keyword co-occurrence on sentiment analysis. Therefore, this study presents a survey of sentiment analysis focusing on the evolution of research methods and topics. It incorporates keyword co-occurrence analysis with a community detection algorithm. This survey not only compares and analyzes the connections between research methods and topics over the past two decades but also uncovers the hotspots and trends over time, thus providing guidance for researchers. Furthermore, this paper presents broad practical insights into the methods and topics of sentiment analysis, while also identifying technical directions, limitations, and future work.

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1 Introduction

Web 2.0 has driven the proliferation of user-generated content on the Internet. This content is closely related to the lives, emotions, and opinions of users. Therefore, analysis of this user-generated data is beneficial for monitoring public opinion and assisting in making decisions. Sentiment analysis, as one of the most popular applications of text-based analytics, can be used to mine people’s attitudes, emotions, appraisals, and opinions about issues, entities, topics, events, and products (Cambria et al. 2022a , b , c , d ; Injadat et al. 2016 ; Jiang et al. 2017 ; Liang et al. 2022 ; Oueslati et al. 2020 ; Piryani et al. 2017 ). Sentiment analysis can help us interpret emotions in unstructured texts as positive, negative, or neutral, and even calculate how strong or weak the emotions are. Today, sentiment analysis is widely used in various fields, such as business, finance, politics, education, and services. This analytical technique has gained broad acceptance not only among researchers but also among governments, institutions, and companies (Khatua et al. 2020 ; Liu et al. 2012 ; Sánchez-Rada and Iglesias 2019 ; Wang et al. 2020b ). It helps policy leaders, businessmen, and service people make better decisions.

The majority of user-generated content data is unstructured text, which increases the great difficulty of sentiment analysis. Since 2000, researchers have been exploring techniques and methods to enhance the accuracy of such analysis. The popularity of social media platforms has brought people around the world closer together. With the continuous advancement of technology, the research topics, application fields, and core methods and technologies of sentiment analysis are also constantly changing.

Comparing and analyzing papers from specific disciplines can help researchers gain a comprehensive understanding of the field. There have been many surveys on sentiment analysis (Nair et al. 2019 ; Obiedat et al. 2021 ; Raghuvanshi and Patil 2016 ). However, there is a lack of adequate discussion on the connections between research methods and topics in the field, as well as on their evolution over time. In 1983, Callon et al. proposed co-word analysis (Callon et al. 1983 ). It can effectively reflect the correlation strength of information items in text data. Co-word analysis based on the frequency of co-occurrence of keywords used to describe papers can reveal the core contents of the research in specific fields. An evolutionary analysis of the associations between core contents is helpful for a comprehensive understanding of the research hotspots and frontiers in the field (Deng et al. 2021 ). It can provide guidance for researchers, especially those who are new to the field, and help them determine research directions, avoid repetitive research, and better discover and grasp the research trends in this field (Wang et al. 2012 ). To fill in the gap in existing research, we conduct keyword co-occurrence analysis and evolution analysis with informetric tools to explore the research hotspots and trends of sentiment analysis.

The main contributions of this survey are as follows:

Using keyword co-occurrence analysis and the informetric tools, the paper presents a survey on sentiment analysis, explores and discovers useful information.

A keyword co-occurrence network is constructed by combining the paper title, abstract, and author keywords. Through the keyword co-occurrence network and community detection algorithm, the research methods and topics in the field of sentiment analysis, along with their evolution in the past two decades, are discussed.

The paper summarizes the research hotspots and trends in sentiment analysis. It also highlights practical implications and technical directions.

The remainder of this paper is organized as follows: In Sect.  2 , we summarize and analyze the existing surveys on sentiment analysis and present the research purpose and methodologies of this paper. Section  3 details the survey methodology, including the collection and processing of scientific publications, visualization, and analysis using different methods and tools. In Sect.  4 , we analyze the results obtained from the keyword co-occurrence analysis and evolution analysis, along with the research hotspots and trends in sentiment analysis identified through the analysis results. Finally, in Sect.  5 , we summarize the research conclusions as well as the practical implications and technical directions of sentiment analysis. We also clarify the limitations of this paper and make suggestions for future work.

2 Existing surveys on sentiment analysis

Sentiment analysis is a concept encompassing many tasks, such as sentiment extraction, sentiment classification, opinion summarization, review analysis, sarcasm detection or emotion detection, etc. Since the 2000s, sentiment analysis has become a popular research field in natural language processing (Hussein 2018 ). In the existing surveys, the researchers mainly conducted specific analyses of the tasks, technologies, methods, analysis granularity, and application fields involved in the sentiment analysis process.

2.1 Surveys on contents and topics of sentiment analysis

When research on sentiment analysis was still in its infancy, the contents and topics of surveys mainly focused on sentiment analysis tasks, analysis granularity, and application areas. Kumer et al. reviewed the basic terms, tasks, and levels of granularity related to sentiment analysis (Kumar and Sebastian 2012 ). They also discussed some key feature selection techniques and the applications of sentiment analysis in business, politics, recommender systems and other fields. Nassirtoussi et al. explored the application of sentiment analysis in market prediction (Nassirtoussi et al. 2014 ). Medhat et al. analyzed the improvement of the algorithms proposed in 2010–2013 and their application fields (Medhat et al. 2014 ). Ravi et al. analyzed the papers related to opinion mining and sentiment analysis from 2002 to 2015. Their study mainly discussed the necessary tasks, methods, applications, and unsolved problems in the field of sentiment analysis (Ravi and Ravi 2015 ).

Existing surveys of the applications of sentiment analysis have focused more on the domains of market research, medicine, and social media in recent years. Rambocas et al. examined the application of sentiment analysis in marketing research from three main perspectives, including the unit of analysis, sampling design, and methods used in sentiment detection and statistical analysis (Rambocas and Pacheco 2018 ). Cheng et al. summarized techniques based on semantic, sentiment, and event extraction, as well as hybrid methods employed in stock forecasting (Cheng et al. 2022 ). Yue et al. categorized and compared a large number of techniques and approaches in the social media domain. That study also introduced different types of data and advanced research tools, and discussed their limitations (Yue et al. 2019 ). In the context of the COVID-19 epidemic, Alamoodi et al. reviewed and analyzed articles on the occurrence of different types of infectious diseases in the past 10 years. They reviewed the applications of sentiment analysis from the identified 28 articles, summarizing the adopted techniques such as dictionary-based models, machine learning models, and mixed models (Alamoodi et al. 2021b ); Alamoodi et al. also conducted a review of the applications of sentiment analysis for vaccine hesitancy (Alamoodi et al. 2021a ). Researchers also reviewed the application of sentiment analysis in the fields of election prediction (Brito et al. 2021 ), education (Kastrati et al. 2021 ; Zhou and Ye 2020 ) and service industries (Adak et al. 2022 ).

Quite a number of research works investigated sentiment analysis works in non-English languages. Sentiment analysis in Chinese (Peng et al. 2017 ), Arabic (Al-Ayyoub et al. 2019 ; Boudad et al. 2018 ; Nassif et al. 2021 ; Oueslati et al. 2020 ), Urdu (Khattak et al. 2021 ), Spanish (Angel et al. 2021 ), and Portuguese (Pereira 2021 ) were conducted. They mainly reviewed the classification frameworks of the sentiment analysis process, supported language resources (dictionaries, natural language processing tools, corpora, ontologies, etc.), and deep learning models used (CNN, RNN, and transfer learning) for each of the languages involved.

2.2 Surveys on methods of sentiment analysis

Before machine learning technology became mature, researchers were particularly concerned about feature extraction methods. For example, Feldman summarized methods for extracting preferred entities from indirect opinions and methods for dictionary acquisition (Feldman 2013 ). Asghar et al. reviewed the natural language processing techniques for extracting features based on part of speech and term position; statistical techniques for extracting features based on word frequency and decision tree model; and techniques for combining part of speech tagging, syntactic feature analysis, and dictionaries (Asghar et al. 2014 ). Koto et al. discussed the best features for Twitter sentiment analysis prior to 2014 by comparing 9 feature sets (Koto and Adriani 2015 ). They found that the current best features for sentiment analysis of Twitter texts are AFINN (a list of English terms used for sentiment analysis manually rated by Finn Årup Nielsen) (Nielsen 2011 ) and Senti-Strength (Thelwall et al. 2012 ). Taboada sorted out the characteristics of words, phrases, and sentence patterns in sentiment analysis from the perspective of linguistics (Taboada 2016 ). Besides, Schouten and Frasinar conducted a comprehensive and in-depth critical evaluation of 15 sentiment analysis web tools (Schouten and Frasincar 2015 ). Medhat et al. ( 2014 ) and Ravi et al. (Ravi and Ravi 2015 ) also analyzed the early algorithms for sentiment analysis.

In the study by Schouten et al., the authors focused on aspect-level sentiment analysis, combing the techniques of aspect-level sentiment analysis before 2014, such as frequency-based, syntax-based, supervised machine learning, unsupervised machine learning, and hybrid approaches. They concluded that the latest technology was moving beyond the early stages (Schouten and Frasincar 2015 ). As research into sentiment analysis became more and more popular and there was important progress made in the development of deep learning technologies, researchers started to pay more attention to the techniques and methods of sentiment analysis. Deep learning methods in particular became the focus of discussions among researchers.

Prabha et al. analyzed various deep learning methods used in different applications at the level of sentence and aspect/object sentiment analysis, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-term Memory (LSTM) (Prabha and Srikanth 2019 ). They discussed the advantages and disadvantages of these methods and their performance parameters. Ain et al. introduced deep learning techniques such as Deep Neural Network (DNN), CNN and Deep Belief Network (DBN) to solve sentiment analysis tasks like sentiment classification, cross-lingual problems, and product review analysis (Ain et al. 2017 ). Zhang et al. investigated deep learning and machine learning techniques for sentiment analysis in the contexts of aspect extraction and categorization, opinion expression extraction, opinion holder extraction, sarcasm analysis, multimodal data, etc. (Zhang et al. 2018 ). Habimana et al. compared the performance of deep learning methods on specific datasets and proposed that performance could be improved using models including Bidirectional Encoder Representations from Transformers (BERT), sentiment-specific word embedding models, cognitive-based attention models, and commonsense knowledge (Habimana et al. 2020 ). Wang et al. reviewed and discussed existing analytical models for sentiment classification and proposed a computational emotion-sensing model (Wang et al. 2020b ).

Some researchers also discussed web tools (Zucco et al. 2020 ), fuzzy logic algorithms (Serrano-Guerrero et al. 2021 ), transformer models (Acheampong et al. 2021 ), and sequential transfer learning (Chan et al. 2022 ) for sentiment analysis.

2.3 Overall survey methodology

With the increase in the popularity of sentiment analysis research, more related research results began to accumulate. Researchers needed to systematically organize and analyze results from a large number of publications to perform literature reviews. They used different survey methodologies to conduct surveys of a large number of papers.

Content analysis is a powerful approach to characterizing the contents of each study by carefully reading its content and manually identifying, coding, and organizing key information in it. A literature review is formed as a result of the repeated use of this approach (Elo and Kyngäs 2008 ; Stemler 2000 ). Content analysis has been used for different studies and systematic reviews (Qazi et al. 2015 , 2017 ). For example, Birjali et al. have studied the most commonly used classification techniques in sentiment analysis from a large amount of literature and introduced the application areas and sentiment classification processes, including preprocessing and feature selection (Birjali et al. 2021 ). They conducted a comprehensive analysis of the papers, discovering that supervised machine learning algorithms are the most commonly used techniques in the field. A complete review of methods and evaluation for sentiment analysis tasks and their applications was conducted by Wankhade et al. ( 2022 ). They compared the strengths and weaknesses of the methods, and discussed the future challenges of sentiment analysis in terms of both the methods and the forms of the data. Although this method can review the research contents and penetrate into the cores of the papers most systematically, it requires a considerable amount of manpower and time for in-depth literature reading.

The systematic literature review guideline proposed by Kitchenham and Charters has gradually attracted the attention of researchers (Kitchenham 2004 ; Kitchenham and Charters 2007 ; Sarsam et al. 2020 ). This review process is divided into six stages: research question definition, search strategy formulation, inclusion and exclusion criteria definition, quality assessment, data extraction, and data synthesis. Researchers can eliminate a large number of retrieved papers by using this standard process and finally conducting further analysis and research on a small number of papers. Kumar et al. reviewed context-based sentiment analysis in social multimedia between 2006 and 2018. From the 573 papers retrieved in the initial search, they finally selected 37 papers to use in discussing sentiment analysis techniques (Kumar and Garg 2020 ). This approach was also used by Kumar et al. in their research on sentiment analysis on Twitter using soft computing techniques. They selected 60 articles out of 502 for follow-up analysis (Kumar and Jaiswal 2020 ). Zunic et al. selected 86 papers from 299 papers retrieved in the period 2011–2019 to discuss the application of sentiment analysis techniques in the field of health and well-being (Zunic et al. 2020 ); Ligthart et al. followed Kitchenham’s guideline and identified 14 secondary studies. They provided an overview of specific sentiment analysis tasks and of the features and methods required for different tasks (Ligthart et al. 2021 ). Obiedat (Obiedat et al. 2021 ), Angel (Angel et al. 2021 ) and Lin (Lin et al. 2022 ) also all followed this guideline to select literature for further analysis. This method can reduce the amount of literature that requires in-depth reading, but in the case of a large amount of literature, more effort is still required to search and screen the material than in traditional literature review methods (Kitchenham and Charters 2007 ).

There are also a few authors who have used informetric methods to review papers. Piryani et al. conducted an informetric analysis of research on opinion mining and sentiment analysis from 2000 to 2015 (Piryani et al. 2017 ). The authors used social network analysis, literature co-citation analysis, and other methods in the paper. They analyzed publication growth rates; the most productive countries, institutions, journals, and authors; and topic density maps and keyword bursts, among other elements. To a certain extent, they interpreted core authors, core papers, areas of research focus in this field, and the current state of national cooperation. In order to explore the application of sentiment analysis in building smart societies, Verma collected 353 papers published between 2010 and 2021 (Verma 2022 ). Using a topic analysis perspective combined with the Louvain algorithm, the author identified four sub-topics in the research field. Similarly, Mantyla et al. employed LDA techniques and manual classification to explore the topic structures of sentiment analysis articles (Mäntylä et al. 2018 ). The informetric methods use natural language processing technologies to intuitively conduct topic mining and analysis of a large number of papers. Through topic clustering, the literature is organized and analyzed, which reduces the time researchers spend on reading the literature in depth. These methods are suitable for exploring research topics and trends in the field.

2.4 Summary of advantages and disadvantages of the existing surveys

In the following, we discuss the advantages and disadvantages of the existing surveys from a number of different points of view.

2.4.1 From the point of view of the contents and topics of sentiment analysis

As summarized in Table 1 , the researchers organized the literature and conducted depth investigations of the contents and topics of sentiment analysis. They reviewed the tasks of sentiment analysis (e.g., different text granularity, opinion mining, spam review detection, and emotion detection), the application areas of sentiment analysis (e.g. market, medicine, social media, and election prediction), and different languages for sentiment analysis, such as Chinese, Spanish, and Arabic (Adak et al. 2022 ; Al-Ayyoub et al. 2019 ; Alamoodi et al. ( 2021a , b ); Alonso et al. 2021 ; Angel et al. 2021 ; Boudad et al. 2018 ; Brito et al. 2021 ; Cheng et al. 2022 ; Hussain et al. 2019 ; Kastrati et al. 2021 ; Khattak et al. 2021 ; Koto and Adriani 2015 ; Kumar and Sebastian 2012 ; Ligthart et al. 2021 ; Medhat et al. 2014 ; Nassif et al. 2021 ; Nassirtoussi et al. 2014 ; Oueslati et al. 2020 ; Peng et al. 2017 ; Pereira 2021 ; Rambocas and Pacheco 2018 ; Ravi and Ravi 2015 ; Schouten and Frasincar 2015 ; Sharma and Jain 2020 ; Yue et al. 2019 ; Zhou and Ye 2020 ). They summarized the methods and application prospects of sentiment analysis under different contents and topics. As the field has grown, new topics have emerged, and knowledge from other fields has been gradually integrated into it. In recent years, the popularity of social media has aroused increasing interest in sentiment analysis research, and the number of papers published, especially those related to different topics of sentiment analysis, has grown rapidly. However, the existing surveys cover a short time range, and there has not been a survey dedicated to the evolution of research contents or topics of sentiment analysis. There have also been few survey works analyzing the connections between topics and methods, or their evolution (e.g., how the contents and topics of sentiment analysis have changed over time).

2.4.2 From the point of view of the methods of sentiment analysis

Some researchers reviewed different techniques and methods of sentiment analysis in different application areas and tasks. They analyzed and discussed sentiment analysis methods based on lexicons, rules, part of speech, term position, statistical techniques, supervised and unsupervised machine learning methods, as well as deep learning methods like LSTM, CNN, RNN, DNN, DBN, BERT, and other hybrid approaches (Acheampong et al. 2021 ; Ain et al. 2017 ; Alamoodi et al. 2021b ; Asghar et al. 2014 ; Chan et al. 2022 ; Cheng et al. 2022 ; Feldman 2013 ; Habimana et al. 2020 ; Koto and Adriani 2015 ; Kumar, Akshi and Sebastian 2012 ; Medhat et al. 2014 ; Prabha and Srikanth 2019 ; Ravi and Ravi 2015 ; Schouten and Frasincar 2015 ; Serrano-Guerrero et al. 2021 ; Taboada 2016 ; Wang et al. 2020b ; Yue et al. 2019 ; Zhang et al. 2018 ; Zucco et al. 2020 ). These researchers also compared the advantages and disadvantages of each method. As summarized in Table 1 , even though existing surveys analyze the techniques and methods of sentiment analysis, providing good insights, there has not been a survey that analyzes the evolution of research methods over time. There have also been few survey works that focuses on the connections between topics and methods of sentiment analysis, and their evolution over time.

2.4.3 From the point of view of the overall survey methodology

The survey methods used have mainly been the content analysis method, Kitchenham and Charters' guideline, and the informetric methods. As summarized in Table 1 , the content analysis method can effectively analyze the contents of research papers in depth, but it does not address the issue of the evolution of the research methods and topics (Bengtsson 2016 ; Birjali et al. 2021 ; Elo and Kyngäs 2008 ; Krippendorff 2018 ; Qazi et al. 2015 , 2017 ; Wankhade et al. 2022 ). Although the number of papers that need to be read in depth can be reduced by following Kitchenham and Charters' guideline, more effort is needed to search and screen literature than in traditional literature review methods (Angel et al. 2021 ; Kitchenham 2004 ; Kitchenham and Charters 2007 ; Kumar and Garg 2020 ; Ligthart et al. 2021 ; Lin et al. 2022 ; Obiedat et al. 2021 ; Sarsam et al. 2020 ; Zunic et al. 2020 ). The informetric methods are best suited to investigating the research methods and topics of sentiment analysis (Bar-Ilan 2008 ; Mäntylä et al. 2018 ; Piryani et al. 2017 ; Santos et al. 2019 ; Verma 2022 ). There are three surveys using informetric techniques and tools that are well suited for analysis of a large number of papers over many years (Mäntylä et al. 2018 ; Piryani et al. 2017 ; Verma 2022 ). However, the evolution of research methods and topics of sentiment analysis over time has not been studied with informetric methods. There have also been few survey works that leverages keyword co-occurrence analysis and community detection to analyze the connections between research methods and topics, and their evolution over time.

Therefore, to address the gaps in the existing surveys, this study presents a survey on the research methods and topics, and their evolution over time. It combines keyword co-occurrence analysis and informetric analysis tools to reveal the methods and topics of sentiment analysis and their evolution in this field from 2002 to 2022.

The following section, Sect.  3 , describes our proposed survey methodology in detail.

3 The proposed survey methodology

This section describes our proposed survey methodology, including collection of scientific publications, processing of scientific publications, as well as visualization and analysis using different methods and tools. The overall scheme of this survey (Fig.  2 ) is also presented in the end of Sect.  3 to better visualize and summarize the proposed survey methodology in this research.

3.1 Collection of scientific publications

We collected research data from the Web of Science platform. We used keywords such as "sentiment analysis," "sentiment mining," and "sentiment classification" to search for relevant papers as data samples. In examining the retrieved papers, we found that some paper topics, paper types, and publication journals were not related to sentiment analysis, so we excluded them. The papers we included were mainly related to the sentiment analysis of texts. We excluded papers on sentiment analysis related to image processing, video processing, speech processing, biological signal processing, etc. Therefore, the retrieval strategy was as follows:

Topic Search (TS) = ("sentiment analy*" or "sentiment mining" or "sentiment classification") And Abstract (AB) = "sentiment" NOT TS = ("face image*" or "speech recognition" or "speech emotion" or "physiological signal*" or "music emotion*" or "facial feature extraction" or "video emotion" or "electroencephalography " or "biosignal*" or "image process*") NOT Title = ("facial" or "speech" or "sound*" or "face" or "dance" or "temperature" or "image*" or "spoken" or "electroencephalography" or "EEG" or "biosignal*" or "voice*" not AB = "facial."

The results in conferences are given the same relevance as journal papers. We chose four databases in the Web of Science: two conference citation databases (Conference Proceedings Citation Index—Social Sciences & Humanities [CPCI-SSH], and Conference Proceedings Citation Index—Science [CPCI-S]), and two journal citation databases (Science Citation Index Expanded [SCI-Expanded] and Social Sciences Citation Index [SSCI]). Given the various forms of words such as "analyzing" and "analysis," a truncated search technique (marked with an asterisk) was used to prevent the omission of relevant papers. The time frame of the retrieved papers was from January 2002 to January 2022, and the publication types of the papers included "article," "conference paper," "review," and "edited material." A total of 9,714 papers were obtained from the four databases above. These included 3,809 articles, 5,633 proceeding papers, 267 reviews, and 5 pieces of editorial material from 2002 to 2022. Overall, there were 104 papers from January 2022. The number of papers each year from 2002 to 2021 is shown in Fig.  1 .

The number of papers each year from 2002 to 2021

3.2 Processing of scientific publications

In this process, our purpose was to extract the key contents of the papers, which are used to analyze the research methods and topics in the field of sentiment analysis. Due to their limited number, the author keywords in each paper often cannot fully represent the key content of the paper. We found that combining the title and abstract could better reflect the core information. Therefore, we synthesized the title, abstract, and author keywords of each paper to extract keywords that represented the main research method and topic of the paper involved using KeyBERT Footnote 1 . KeyBERT is a keyword extraction technique that uses BERT embedding to create keywords and key phrases that most closely resemble document content (Grootendorst and Warmerdam 2021 ). The specific keyword extraction process was as follows:

First, we used KeyBERT to extract 8 keywords and eliminated keywords with a weight lower than 0.3. We then combined the extracted keywords with the author keywords and removed duplicates. After that, we standardized the whole collection of keywords and merged synonyms. Finally, we counted the number of keywords and removed meaningless terms like "sentiment analysis," "sentiment classification," and "sentiment mining."

After statistical analysis, we obtained 41,827 keywords with a total word frequency of 88,104. As there were 9,714 papers and 41,827 keywords, we found that most of the keywords with word frequency below 10 were not representative of the research contents of sentiment analysis. As a result, a total of 685 representative keywords were reserved for subsequent analysis. These keywords appeared a total of 30,801 times. Table 2 shows the keywords with word frequency in the top 50.

High-frequency keywords generally represent research hotspots. We therefore extracted high-frequency keywords to serve as the basis for the subsequent analysis. We found that most of the keywords with word frequency 18 and lower, such as "ranking," "mask," "experience," "affect," "online forum," and so on, were not relevant to sentiment analysis. Therefore, the keywords with a word frequency higher than 18 were reserved for analysis. These keywords appeared 25,429 times in the collected data, accounting for close to 83% of all the keywords. We obtained 275 keywords, which were used to analyze the main methods and topics of sentiment analysis.

3.3 Visualization and analysis using different methods and tools

3.3.1 analytical methods.

Keywords are the core natural language vocabulary to express the subject, content, ideas, and research methods of the literature (You et al. 2021 ). Keywords represent the topics of the domain, and cluster analysis of these words can reflect the structure and association of topics. Keyword co-occurrence analysis counts the number of occurrences of a set of keywords in the same document. The strength and number of associations between research contents can be obtained through keyword co-occurrence analysis. Dividing research methods and topics into sub-communities helps researchers to analyze hotspots and trends in methods and topics, as well as to obtain sub-fields of sentiment analysis research (Ding et al. 2001 ).

3.3.2 Visualization and analysis tools

BibExcel Footnote 2 is a software tool for analyzing bibliographic data or any text-based data formatted in a similar way (Persson 2017 ). The tool generates structured data files that can be read by Excel for subsequent processing (Persson et al. 2009 ). Our processing steps are as follows. First, we imported the standardized bibliographic data into BibExcel. This tool can help structure the data. Second, we checked and corrected the data and used BibExcel to count the number of co-occurrences of keywords.

We then used Pajek Footnote 3 software to visualize the keyword co-occurrence network and divided the sub-communities. Pajek is a large and complex network analysis tool (Batagelj and Andrej 2022 ; Batagelj and Mrvar 1998 ). It can calculate certain indicators to reveal the state and properties of the network involved. In addition, Pajek’s Louvain community detection algorithm can help divide the keyword co-occurrence network into sub-communities, which represent sub-fields of sentiment analysis (Blondel et al. 2008 ; Leydesdorff et al. 2014 ; Rotta and Noack 2011 ). The Louvain community-detection algorithm unfolds a complete hierarchical community structure for the network. It has an advantage in subdividing different areas of study: multiple knowledge structures and details can be shown in one network (Deng et al. 2021 ).

After that, we applied VOSviewer Footnote 4 to optimize the visualization of sub-communities (Van Eck and Waltman 2010 ; VOSviewer 2021 ; Perianes-Rodriguez et al. 2016 ; Waltman and Van Eck 2013 ; Waltman et al. 2010 ). VOSviewer can help display the core keywords in each sub-community and the correlation between keywords. It can also reflect the closeness of the association between sub-communities. Finally, we used Excel to count the frequency of keywords for each year and to map the evolution of research methods and topics in the field of sentiment analysis.

3.3.3 Graphical representation of the overall scheme of this survey

This paper proposes and conducts a new research survey on sentiment analysis. The graphical representation of the overall scheme of this survey is shown in Fig.  2 . The main scheme includes four modules: Module A, Collection of scientific publications; Module B, Processing of scientific publications; Module C, Visualization and analysis through different methods and tools, and Module D, Result analysis and discussions based on various aspects.

Graphical representation of the overall scheme of this survey. Module A: Collection of scientific publications; Module B: Processing of scientific publications; Module C: Visualization and analysis using different methods and tools; Module D: Result analysis and discussions considering various aspects

In Module A, scientific publications are collected from the Web of Science (WOS) platform, as has been detailed in Sect.  3.1 Collection of scientific publications above. Module B, Processing of scientific publications, has been detailed in Sect.  3.2 above. It performs a data processing procedure to obtain key information, which includes all the representative keywords and high-frequency keywords. The title, abstract and keywords of the papers are used to extract such key information using KeyBERT (Grootendorst and Warmerdam 2021 ). Such key information is analyzed and visualized through different methods, including different visualization tools, as introduced in Sect.  3.3 (Module C), Visualization and analysis using different methods and tools, above.

In Module C, the number of co-occurrences of keywords is obtained using BibExcel (Persson 2017 ), the co-occurrences of keywords are analyzed and visualized using Pajek (Blondel et al. 2008 ; Leydesdorff et al. 2014 ; Rotta and Noack 2011 ) and VOSviewer (Van Eck and Waltman 2010 ; VOSviewer 2021 ; Perianes-Rodriguez et al. 2016 ; Waltman and Van Eck 2013 ; Waltman et al. 2010 ). The keyword community network and the keyword community evolution are analyzed and visualized using these tools, as described in Sect.  3.3 (Module C), Visualization and analysis using different methods and tools. According to the visualization and analysis results obtained in Module C, Module D, Result analysis and discussions, will be detailed in Sect.  4 .

In the following section, Sect.  4 (Module D), results are analyzed and discussed considering various aspects, including the research methods and topics of sentiment analysis in each community, the evolution of research methods and topics along with the research hotspots and trends over time.

4 Results and analysis through various aspects

4.1 research methods and topics of sentiment analysis, 4.1.1 overall characteristic analysis.

The high-frequency keywords were presented in Table 2 . These keywords can be regarded as the main research contents in the field of sentiment analysis. "Twitter" ranks at the top. It is followed by "opinion mining," "natural language processing," "machine learning," and so on. The high-frequency keywords cover the topics of the studies, the contents of the studies, and the techniques and methods used. Based on these keywords, we used Pajek’s Louvain method to construct a keyword co-occurrence network to represent the research methods and topics as shown in Fig.  3 . The keyword co-occurrence network is divided into six communities. The research methods and topics of the six communities include social media platforms (C1), machine learning methods (C2), natural language processing and deep learning methods (C3), opinion mining and text mining (C4), Arabic sentiment analysis (C5), and others, such as domain sentiment analysis and transfer learning, etc. (C6).

Keyword community network

In Fig.  3 , the size of the node represents the number of keywords. The thickness of the line between the nodes represents the number of collaborations between keywords. The top 20 keywords in each community are sorted in descending order, as shown in Table 3 . The keyword co-occurrence network features of the six sub-communities are described in Table 4 . The number of nodes shows the number of keywords in each community, and the number of links shows the correlations between the keywords.

As shown in Table 4 , we can see from the number of links between sub-communities that there is a strong correlation between them, especially the link between C3 and C4, which has 1306 lines. The reason may be that the research methods of C4 focus on "opinion mining" and "text mining," while those of C3 focus on "natural language processing" and "deep learning," and C3 provides more technical support for C4 research. In C5 and C6, the research methods and topics are scattered. Their internal links are also low, but the connections with C3 and C4 are relatively high. The contents of C5 and C6 may include some emerging research methods and topics. We will present a specific analysis on the methods and topics of each sub-community in the next subsection.

4.1.2 Analysis on research methods and topics of sub-communities

4.1.2.1 analysis on research methods and topics of the c1 community.

Figure  4 shows the keyword co-occurrence network of the C1 community. The research methods and topics of the C1 community focus on three areas: "social media," "topic models," and "covid-19." In the context of big data, web 2.0 technology provides users with a way to express reviews and opinions of services, events, and people. Various social media platforms, such as Twitter, YouTube, and Weibo, have a large amount of users’ emotional data (Momtazi 2012 ). Compared to traditional news media, information on social media spreads more quickly, and people are able to express their feelings more freely. It is important to analyze the emotions generated by the information shared and published on social media (Abdullah and Zolkepli 2017 ; Wang et al. 2014 ). Researchers have been extracting text data from social media platforms for years to detect unexpected events (Bai and Yu 2016 ; Preethi et al. 2015 ), improve the quality of products (Abrahams et al. 2012 ; Isah et al. 2014 ; Myslin et al. 2013 ), understand the direction of public opinion (Fink et al. 2013 ; Groshek and Al-Rawi 2013 ), and so on.

The keyword co-occurrence network for the C1 community

Users’ sentiments are often associated with the topics, and the accuracy of sentiment analysis can be improved through the introduction of topic models (Li et al. 2010 ). Among them, the Latent Dirichlet Allocation (LDA) method is cited most frequently. Previous studies found that the LDA method can be effective in subdividing topics and identifying the sentiments of the contents. This method is quite general, and there are also many improved models based on this one that can be applied to any type of web text, helping to enhance the accuracy of sentiment polarity calculation (Chen et al. 2019 ; Liu et al. 2020 ).

As the COVID-19 pandemic has unfolded, a large number of individuals, media and governments have been publishing news and opinions about the COVID-19 crisis on social media platforms. This has resulted in a lot of sentiment analysis studies focusing on COVID-19-related texts exploring the impact of the epidemic on people’s lives (Sari and Ruldeviyani 2020 ; Wang, T. et al. 2020a ), physical health (Berkovic et al. 2020 ; Binkheder et al. 2021 ) and mental health (Yin et al. 2020 ), and so on. Therefore, we can see many related keywords, such as "infodemiology," "healthcare," and "mental health."

4.1.2.2 Analysis on research methods and topics of the C2 community

The contents of the C2 community mainly focus on "machine learning," "text classification," "feature extraction," and "stock market" (see Fig.  5 ). Most keywords are related to the research methods of sentiment analysis. Machine learning approaches have expanded from topic recognition to more challenging tasks such as sentiment classification. It is very important to explore and compare machine learning methods applied to sentiment classification (Li and Sun 2007 ). Methods like Support Vector Machine (SVM) and Naive Bayes models are widely used (Altrabsheh et al. 2013 ; Dereli et al. 2021 ; Shofiya and Abidi 2021 ; Tan et al. 2009 ; Wang and Lin 2020 ) and are used as benchmarks for the comparisons of models proposed by many researchers (Kumar et al. 2021 ; Sadamitsu et al. 2008 ; Waila et al. 2012 ; Zhang et al. 2019 ). Many algorithms, such as random forest (Al Amrani et al. 2018 ; Fitri et al. 2019 ; Sutoyo et al. 2022 ), tf-idf (Arafin Mahtab et al. 2018 ; Awan et al. 2021 ; Dey et al. 2017 ), logistic regression (Prabhat and Khullar 2017 ; Qasem et al. 2015 ; Sutoyo et al. 2022 ), and n-gram (Ikram and Afzal 2019 ; Singh and Kumari 2016 ; Xiong et al. 2021 ) are used to enhance the accuracy of machine learning, as shown in Fig.  5 .

The keyword co-occurrence network for the C2 community

The trading volume and asset prices of financial commodities or financial instruments are influenced by a variety of factors in the online environment. Machine learning and sentiment analysis are powerful tools that can help gather vast amounts of useful information to predict financial risk effectively (Li et al. 2009 ). Research on the relationship between public sentiment and stock prices has always been the focus of many scholars (Smailović et al. 2014 ; Xing et al. 2018 ). They have used machine learning methods to explore the influence of sentiments on stock prices through sentiment analysis of news articles, and then predicted the trend changes in the stock market (Ahuja et al. 2015 ; Januário et al. 2022 ; Maqsood et al. 2020 ; Picasso et al. 2019 ).

4.1.2.3 Analysis on research methods and topics of the C3 community

The contents of the C3 community also mainly focus on the methods for sentiment analysis, like "natural language processing", "deep learning," "aspect-based sentiment analysis," and "task analysis" (Fig.  6 ). Sentiment analysis is a sub-field of natural language processing (Nicholls and Song 2010 ), and natural language processing techniques have been widely used in sentiment analysis. Using natural language processing technology can help to better parse text features, such as part-of-speech tagging, word sense disambiguation, keyword extraction, inter-word dependency recognition, semantic parsing, and dictionary construction (Abbasi et al. 2011 ; Syed et al. 2010 ; Trilla and Alías 2009 ). With the rise of deep learning technology, researchers began to introduce it to sentiment analysis. Neural network models like LSTM (Al-Dabet et al. 2021 ; Al-Smadi et al. 2019 ; Li and Qian 2016 ; Schuller et al. 2015 ; Tai et al. 2015 ), CNN (Cai and Xia 2015 ; Jia and Wang 2022 ; Ouyang et al. 2015 ), RNN (Hassan and Mahmood 2017 ; Tembhurne and Diwan 2021 ; You et al. 2016 ), and some combination of these, as well as other models (An and Moon 2022 ; Li et al. 2022 ; Liu et al. 2020a ; Salur and Aydin 2020 ; Zhao et al. 2021 ), have received significant attention.

The keyword co-occurrence network for the C3 community

Sentiment analysis granularity is subdivided into document level, sentence level, and aspect level. Document-level sentiment analysis takes the entire document as a unit, but the premise is that the document needs to have a clear attitude orientation—that is, the point of view needs to be clear (Shirsat et al. 2018 ; Wang and Wan 2011 ). Sentence-level sentiment analysis is intended to perform sentiment analysis of the sentences in the document alone (Arulmurugan et al. 2019 ; Liu et al. 2009 ; Nejat et al. 2017 ). Aspect-based analysis is a fundamental and significant task in sentiment analysis. The aim of aspect-level sentiment analysis is to separately summarize positive and negative views about different aspects of a product or entity, although overall sentiment toward a product or entity may tend to be positive or negative (Rao et al. 2021 ; Thet et al. 2010 ). Aspect-level sentiment analysis facilitates a more finely-grained analysis of sentiment than either document or sentence-level analysis (Liang et al. 2022 ; Wang et al. 2020c ). The traditional levels of analysis, such as sentence-level analysis can only calculate the comprehensive sentiment polarity of paragraphs or sentences (Wang et al. 2016 ; Zhang et al. 2021 ). In recent years, the aspect level has become more and more popular, and with the application of deep learning technology, it has become better at capturing the semantic relationship between aspect terms and words in a more quantifiable way (Huang et al. 2018 ). The process of sentiment analysis involves the coordination of multiple tasks, and the subtasks include feature extraction (Bouktif et al. 2020 ; Lin et al. 2020 ), context analysis (Yu et al. 2019 ; Zuo et al. 2020 ), and the application of some analytical models (Tan et al. 2020 ).

4.1.2.4 Analysis on research methods and topics of the C4 community

The C4 community mainly shows keywords related to the research methods and topics of "opinion mining" and "user review," which is the largest of the six sub-communities (Fig.  7 ). With the popularity of platforms like online review sites and personal blogs on the Internet, opinions and user reviews are readily available on the web. Opinion mining has always been a hot field of research (Khan et al. 2009 ; Poria et al. 2016 ). From Table 4 , we can see that the link between C3 and C4 has 1306 lines. In opinion mining, researchers use many text mining methods to discover users’ opinions on goods or services, and then help improve the quality of corresponding products or services (Da’u et al. 2020 ; Lo and Potdar 2009 ; Martinez-Camara et al. 2011 ). In addition, scholars have found that the consideration of user opinions can help improve the overall quality of recommender systems (Artemenko et al. 2020 ; Da’u et al. 2020 ; Garg 2021 ; Malandri et al. 2022 ). Therefore, "recommendation system" has a strong correlation with "opinion mining."

The keyword co-occurrence network for C4 community

Evaluation metrics for quantifying the existing approaches are also a popular topic related to opinion mining. There is a keyword named "performance sentiment" in the C4 community. Precision, recall, accuracy and F1-score are the most commonly used evaluation metrics (Dangi et al. 2022 ; Jain et al. 2022 ; JayaLakshmi and Kishore 2022 ; Li et al. 2017 ; Wang et al. 2021 ; Yi and Niblack 2005 ). Some researchers have also used runtimes to calculate the model efficiency (Abo et al. 2021 ; Ferilli et al. 2015 ), p-value to statistically evaluate the relationship or difference between two samples of classification results (JayaLakshmi and Kishore 2022 ; Salur and Aydin 2020 ), paired sample t-tests to verify that the results are not obtained by chance (Nhlabano and Lutu 2018 ), and standard deviation to measure the stability of the model (Chang et al. 2020 ). There have also been researchers who have used G-mean (Wang et al. 2021 ), Pearson Correlation Coefficient (Corr) (Yang et al. 2022 ), Mean Absolute Error (MAE) (Yang et al. 2022 ), Normalized Information Transfer (NIT) and Entropy-Modified Accuracy (EMA) (Valverde-Albacete et al. 2013 ), Mean Squared Error (MSE) (Mao et al. 2022 ), Hamming loss (Liu and Chen 2015 ), Area Under the Curve (AUC) (Abo et al. 2021 ), sensitivity and specificity (Thakur and Deshpande 2019 ), etc.

4.1.2.5 Analysis on research methods and topics of the C5 & C6 communities

Both sub-communities C5 (Fig.  8 ) and C6 (Fig.  9 ) are small in size. The C5 community has 25 nodes and the C6 community has 41 nodes. The core content of the C5 community is "Arabic sentiment analysis." Before 2011, most resources and systems built in the field of sentiment analysis were tailored to English and other Indo-European languages. It is increasingly necessary to design sentiment analysis systems for other languages (Korayem et al. 2012 ), and researchers are increasingly interested in the study of tweets and texts in the Arabic language (Heikal et al. 2018 ; Khasawneh et al. 2013 ; Oueslati et al. 2020 ). They use technologies such as named entity recognition (Al-Laith and Shahbaz 2021 ), deep learning (Al-Ayyoub et al. 2018 ; Heikal et al. 2018 ), and corpus construction (Alayba et al. 2018 ) to enhance the accuracy of sentiment analysis.

The keyword co-occurrence network for the C5 community

The keyword co-occurrence network for the C6 community

The contents of the C6 community are not very concentrated. From the size of the circle, we can see that the keywords "domain adaptation"(Blitzer et al. 2007 ; Glorot et al. 2011 ), "domain sentiment," and "cross-domain" appear more frequently. Cross-domain sentiment classification is intended to address the lack of mass labeling data (Du et al. 2020a ). It has attracted much attention (Du et al. 2020b ; Hao et al. 2019 ; Yang et al. 2020b ). Advances in communication technology have provided valuable interactive resources for people in different regions, and the processing of multilingual user comments has gradually become a key challenge in natural language processing (Martinez-Garcia et al. 2021 ). Therefore, some keywords related to "lingual" have appeared. Other keywords, such as "transfer learning," "active learning," and "semi-supervised learning," are mainly related to sentiment analysis technologies.

4.2 Evolution of research methods and topics of sentiment analysis

4.2.1 overall evolution analysis.

Annual changes in keyword frequency in sentiment analysis research can reflect the evolution of research methods and topics in this field. Based on the keyword community network (Fig.  3 ), we counted the frequency of keywords in each sub-community for each year. The keyword community evolution diagram is shown in Fig.  10 . Since there were fewer papers published before 2006, we combined the occurrences of keywords from 2002 to 2006. We can see that the C1 community and the C3 community have shown a significant growth trend. The C2 community was in a state of growth until 2019, and the frequency of keywords decreased year by year after 2019. The frequency of C4 community keywords continued to increase until 2018 and declined after 2018. The number of keywords in the C5 community and in the C6 community both had a slow growth trend, but the trend was not obvious.

Keyword community evolution diagram

4.2.2 Evolution analysis of sub-communities

We selected the high-frequency keywords under each category and plotted the change of word frequency in each year, as shown in Figs.  11 and 12 . In the C1 community, "social medium," "Twitter," "social network," "covid-19," "Latent Dirichlet Allocation," "topic model," and "text analysis" all had significant increases in word frequency, and the growth trend in 2021 was obvious. "Covid-19" appears in 2020, and the word frequency increased rapidly in 2021. Social media platforms have always been the focus of researchers’ attention. Under the influence of COVID-19, more people express their emotions, stress, and thoughts through social media platforms. Sentiment analysis on data from social media platforms related to COVID-19 has become a hot topic (Boon-Itt and Skunkan 2020 ). We believe that due to the impact of COVID-19, the widespread use of social platforms in 2020–2021 has led to a surge in the number of C1-related keywords.

C1, C2, C5, C6 communities: High-frequency keyword evolution diagram

C3, C4 communities: High-frequency keyword evolution diagram

The C2 community focuses on the method of "machine learning," and the C3 community focuses on the methods of "deep learning" and "natural language processing." The keywords in the two communities are mainly related to the techniques and methods of sentiment analysis. We have found that before 2016 (Fig.  10 ), the frequency of keywords in the C2 community was higher than that in the C3 community, and in 2016 and later, the frequency of keywords in the C3 community gradually accounted for a larger proportion of the total. This reflects the fact that deep learning-related technologies and methods have become a research hotspot, and the attention given to SVM, Naive Bayes, supervised learning, and other technologies in machine learning has declined. In addition to deep learning models such as Bi-LSTM, Long Short-term Memory, and recurrent neural network in the C3 community, the number of "aspect based" and "feature extraction" keywords have also been growing, which shows that researchers now pay more attention to the aspect level of text granularity in the field of sentiment analysis.

Among the keywords found in the C4 community, the word frequency of the "opinion mining" keyword has decreased since 2018. This shows that in the field of sentiment analysis, researchers have begun to reduce the attention they give to sentiment analysis of opinions on product or service quality, while still maintaining a certain degree of attention to "user review" and "online review." In addition, the number of keywords for "sentiment lexicon" and "lexicon-based" has declined. It may be because, in the context of the widespread application of deep learning technology in recent years, the lexicon-based method requires more time and higher labor costs (Kaity and Balakrishnan 2020 ). However, its accuracy still attracts attention due to the high involvement of experts, especially in non-English languages (Bakar et al. 2019 ; Kydros et al. 2021 ; Piryani et al. 2020 ; Tammina 2020 ; Xing et al. 2019 ; Yurtalan et al. 2019 ).

The high-frequency keywords in the C5 and C6 communities are "Arabic language," "Arabic sentiment analysis," and "transfer learning." Arabic has 30 variants, including the official Modern Standard Arabic (MSA) (ISO 639–3 2017). Arabic dialects are becoming increasingly popular as the language of informal communication on blogs, forums, and social media networks (Lulu and Elnagar 2018 ). This makes them challenging languages for natural language processing and sentiment analysis (Alali et al. 2019 ; Elshakankery and Ahmed 2019 ; Sayed et al. 2020 ). Transfer learning can solve the problem by leveraging knowledge obtained from a large-scale source domain to enhance the classification performance of target domains (Heaton 2018 ). In recent years, based on the success of deep learning technology, this method has gradually attracted attention.

5 Research hotspots and trends

Through the analysis in Sects.  4.1 and 4.2 , we found that the research methods and topics of sentiment analysis are constantly changing. The keyword topic heat map is shown in Fig.  13 . From this map, we can see that in the past two decades, research hotspots have included social media platforms (such as "social medium," "social network," and "Twitter"); sentiment analysis techniques and methods (such as "machine learning," "svm," "natural language processing," "deep learning," "aspect-based," "text mining," and "sentiment lexicon"), mining of user comments or opinions (e.g., "opinion mining," "user review," and "online review"), and sentiment analysis for non-English languages (e.g., "Arabic sentiment analysis" and "Arabic language").

Keyword topic heat map

With the popularity of digitization, a large amount of user-generated content has appeared on the Internet, where users express their opinions and comments on different topics such as the news, events, activities, products, services, etc. through social media. This is especially so in the case of the Twitter mobile platform, launched in 2006, which has become the most popular social channel (Kumar and Jaiswal 2020 ). However, online text data is mostly unstructured. In order to accurately analyze users’ sentiments, the research methods for sentiment analysis, such as natural language processing technology, and automatic sentiment analysis models have become the focus of researchers’ works. From Fig.  11 , we can see that early technologies and methods are dominated by machine learning and that SVM and Naive Bayes have always been favored by researchers. This has also been confirmed in studies by Neha Raghuvanshi (Raghuvanshi and Patil 2016 ), Harpreet Kaur (Kaur et al. 2017 ), and Marouane Birjali (Birjali et al. 2021 ). With the improvement of neural network and artificial intelligence technology, deep learning technology has been widely used in sentiment analysis, and has resulted in good outcomes (Basiri et al. 2021 ; Ma et al. 2018 ; Prabha and Srikanth 2019 ; Yuan et al. 2020 ). However, deep learning technology still has room for improvement, and the hybrid methods combining sentiment dictionary and semantic analysis are gradually becoming a trend (Prabha and Srikanth 2019 ; Yang et al. 2020a ).

The granularity of sentiment analysis ranges from the early text level to the sentence level and finally to the aspect level, which is currently gaining strong attention. The granularity of sentiment analysis is gradually being refined, but the method is immature at present, and further research work in the future is needed (Agüero-Torales et al. 2021 ; Li et al. 2020 ; Trisna and Jie 2022 ).

Early sentiment analysis was mainly in the English language. In recent years, non-English languages such as Chinese (Lai et al. 2020 ; Peng et al. 2018 ), French (Apidianaki et al. 2016 ; Pecore and Villaneau 2019 ), Spanish (Chaturvedi et al. 2016 ; Plaza-del-Arco et al. 2020 ), Russian (Smetanin 2020 ), and Arabic (Alhumoud and Al Wazrah 2022 ; Ombabi et al. 2020 ) have attracted more and more attention. Furthermore, cross-domain sentiment analysis technology is in urgent need of research and discussion by researchers (Liu et al. 2019 ; Singh et al. 2021 ).

6 Conclusion and future work

6.1 conclusion.

Judging from the increasing number of papers related to sentiment analysis research every year, sentiment analysis has been on the rise. Although there are many surveys on sentiment analysis research, there has not been a survey dedicated to the evolution of research methods and topics of sentiment analysis. This paper has used keyword co-occurrence analysis and the informetric tools to enrich the perspectives and methods of previous studies. Its aims have been to outline the evolution of the research methods and tools, research hotspots and trends and to provide research guidance for researchers.

By adopting keyword co-occurrence analysis and community detection methods, we analyzed the research methods and topics of sentiment analysis, as well as their connections and evolution trends, and summarized the research hotspots and trends in sentiment analysis. We found that research hotspots include social media platforms, sentiment analysis techniques and methods, mining of user comments or opinions, and sentiment analysis for non-English languages. Moreover, deep learning technology, with its hybrid methods combining sentiment dictionary and semantic analysis, fine-grained sentiment analysis methods, and non-English language analysis methods, and cross-domain sentiment analysis techniques have gradually become the research trends.

6.2 Practical implications and technical directions of sentiment analysis

Sentiment analysis has a wide range of application targets, such as e-commerce platforms, social platforms, public opinion platforms, and customer service platforms. Years of development have led to many related tasks in sentiment analysis, such as sentiment analysis of different text granularity, sentiment recognition, opinion mining, dialogue sentiment analysis, irony recognition, false information detection, etc. Such analysis can help structure user reviews, support product improvement decisions, discover public opinion hotspots, identify public positions, investigate user satisfaction with products, and so on. As long as user-generated content is involved, sentiment analysis technology can be used to mine the emotions of human actors associated with the content. The improvement of sentiment analysis technology can help machines better understand the thoughts and opinions of users, make machines more intelligent, and make better decisions for policy leaders, businessmen, and service people. However, most of the current sentiment analysis methods are based on sentiment dictionaries, sentiment rules, statistics-based machine learning models, neural network-based deep learning models, and pre-training models, and have yet to achieve true language understanding in the sense of comprehension at the deep semantic level, though this does not prevent them from being useful in certain practical applications.

As an important task in natural language understanding, sentiment analysis has received extensive attention from academia and industry. Coarse-grained sentiment analysis is increasingly unable to meet people's decision-making needs, and for aspect-level sentiment analysis and complex tasks, pure machine learning is still unable to flexibly achieve true language understanding. Once the scene or domain changes, problems such as the domain incompatibility of the sentiment dictionary and the low transfer effect of the model involved keep appearing. At present, the accuracy of sentiment analysis provided by machines is far less than that of humans. To achieve human-like performance for machines, we believe that it is necessary to incorporate human commonsense knowledge and domain knowledge, as well as grounded definitions of concepts, in order for machines to understand natural language at a deeper level. These, combined with rules for affective reasoning to supplement interpretable information, will be effective in improving the performance of sentiment analysis. Future research in this direction can be strengthened to achieve true language understanding in machines.

6.3 Limitations and future work

There are some research limitations in this paper. First, we only studied papers written in English and searched from the Web of Science platform. We believe there are papers in other languages or other databases (e.g., Scopus, PubMed, Sci-hub, etc.) that also involve sentiment analysis but that were not included in our study. In addition, the keywords we chose to search in the Web of Science were mainly "sentiment analysis," "sentiment mining," and "sentiment classification." There may be papers related to our research topic that do not have these keywords. To track developments in sentiment analysis research, future studies could replicate this work by employing more precise keywords and using different literature databases.

Second, we selected the main high-frequency keywords for analysis, and some important low-frequency keywords may have been ignored. In future work, we can analyze the changes in each keyword in detail from the perspective of time and obtain more comprehensive analysis results.

Third, the results show that the themes of sentiment analysis cover many fields, such as computer science, linguistics, and electrical engineering, which indicates the trend of interdisciplinary research. Therefore, future work should apply co-citation and diversity measures to explore the interdisciplinary nature of sentiment analysis research.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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The authors would like to thank the China Scholarship Council (CSC No. 202106850069) for its support for the visiting study.

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Cui, J., Wang, Z., Ho, SB. et al. Survey on sentiment analysis: evolution of research methods and topics. Artif Intell Rev 56 , 8469–8510 (2023). https://doi.org/10.1007/s10462-022-10386-z

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