article

Sentiment in Twitter events

Authors:

Georgios PaltoglouAuthors Info & Claims

Journal of the American Society for Information Science and Technology, Volume 62, Issue 2

Pages 406 - 418

https://doi.org/10.1002/asi.21462

Published: 01 February 2011 Publication History

Abstract

The microblogging site Twitter generates a constant stream of communication, some of which concerns events of general interest. An analysis of Twitter may, therefore, give insights into why particular events resonate with the population. This article reports a study of a month of English Twitter posts, assessing whether popular events are typically associated with increases in sentiment strength, as seems intuitively likely. Using the top 30 events, determined by a measure of relative increase in (general) term usage, the results give strong evidence that popular events are normally associated with increases in negative sentiment strength and some evidence that peaks of interest in events have stronger positive sentiment than the time before the peak. It seems that many positive events, such as the Oscars, are capable of generating increased negative sentiment in reaction to them. Nevertheless, the surprisingly small average change in sentiment associated with popular events (typically 1% and only 6% for Tiger Woods' confessions) is consistent with events affording posters opportunities to satisfy pre-existing personal goals more often than eliciting instinctive reactions. © 2011 Wiley Periodicals, Inc.

References

[1]

Allan, J., Papka, R, & Lavrenko, V. (1998). On-line new event detection and tracking In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 37-45). New York: ACM Press.

[2]

Archak, N., Ghose, A., & Ipeirotis, P.G. (2007). Show me the money!: Deriving the pricing power of product features by mining consumer reviews. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 56-65). New York: ACM Press.

[3]

Balog, K, Mishne, G., & Rijke, M. (2006). Why are they excited? Identifying and explaining spikes in blog mood levels. In Proceedings of the Eleventh Meeting of the European Chapter of the Association for Computational Linguistics (EACL 2006) (pp. 207-210). Stroudsburg, PA: ACL.

[4]

Bansal, N., & Koudas, N. (2007). BlogScope: A system for online analysis of high volume text streams. In Proceedings of the 33rd International Conference on Very Large Data Bases (pp. 1410-1413). New York: ACM Press.

[5]

Bassett, E.H., & O'Riordan, K (2002). Ethics of Internet research: Contesting the human subjects research model Ethics and Infonnation Technology, 4(3), 233-247.

[6]

Bifet, A., & Frank, E. (2010). Sentiment kuowledge discovery in Twitter streaming data. In Proceedings of the 13th International Conference on Discovery Science (pp. 1-15). Berlin, Gennany: Springer.

[7]

Blumler, J.G., & Katz, E. (1974). The uses of mass communications: Current perspectives on gratifications research. Beverly Hills, CA: Sage.

[8]

Bollen, J., Pepe, A., & Mao, H. (2009). Modeling public mood and emotion: Twitter sentiment and socioeconomic phenomena. arXiv.org, arXiv:0911.1583v0911 {cs.CYj 0919 Nov 2009.

[9]

boyd, d., Golder, S., & Lotan, G. (2009). Tweet, tweet, retweet: Conversational aspects of retweeting on Twitter. In Proceedings of the 43rd Annual Hawaii International Conference on Systems Science (HICSS-43). Retrieved November 4, 2010, from hup://www.danah.org/papers/ TweetTweetRetweet.pdf

[10]

Bruza, P., & Weeber, M. (2008). Literature-based discovcry. Berlin, Germany: Springer.

[11]

Case, D.O. (2002). Looking for information: A survey of lesearch on information seeking, needs, and behavior. San Diego, CA Academic Press.

[12]

Cataldi, M., Caro, L.D., & Schifanella, C. (2010). Emerging topic detection on Twitter based on temporal and social terms evaluation. In Proceedings of the Tenth International Workshop on Multimedia Data Mining table of contents (Article No.4). New York: ACM Press.

[13]

Cha, M., Haddadi, H., Benevenuto, F., & Gummadi, KP. (2010). Measuring user influence in Twitter: The million follower fallacy. In Proceedings of the International AAAI Conference on Weblogs and Social Media. Menlo Park, CA: Association for the Advancement of Artificial Intelligence. Retrieved November 4, 2010, http://an.kaist.ac.kr/~Inycha/docs/ icwsm2010_cha.pdf

[14]

Choudhury, M.D., Sundaram, H., John, A., & Seligmann, D.D. (2008). Can blog communication dynamics be correlated with stock market activity? In Proceedings of the 19th ACM Conference on Hypertext and Hypermedia (pp. 55-60). New York: ACM Press.

[15]

Culotta, A. (2010). Detecting influenza outbreaks by analyzing Twitter messages. Retrieved November 5, 2010, from http://arxiv.org/PS_cache/arxiv/ pdf/1007/1007.4748v2011.pdf

[16]

Diakopoulos, N.A., & Shamma, D.A. (2010). Characterizing debate performance via aggregated twitter sentiment. In Proceeding of the 28th International Conference on Human Factors in Computing Systems (pp. 1195-1198). New York: ACM Press.

[17]

Dodds, P.S., & Danforth, C.M. (2010). Measuring the Itappiness of large-scale written expression: Songs, blogs, and presidents. Journal of Happiness Studies, 11(4), 441-456.

[18]

Efron, M. (2010). Hashtag retrieval in a microblogging environment. In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 787-788). New York: ACM Press.

[19]

Enyon, R., Schroeder, R., & Fry, J. (2009). New techniques in online research: Challenges for research ethics. 21st Century Society, 4(2), 187-199.

[20]

Fox, E. (2008). Emotion science. Basingstoke, United Kingdom: Palgrave Macmillan.

[21]

Gamon, M., Aue, A., Corston-Oliver, S., & Ringger, E. (2005). Pulse: Mining customer opinions from free text. Lecture Notes in Computer Science, 3646, 121-132.

Digital Library

[22]

Gaver, W.W. (1991). Technology affordances. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 79-84). New York: ACM Press.

[23]

Gibson, J.J. (1977). The theory of affordances. In R Shaw & J. Bransford (Eds.), Perceiving, acting, and knowing: Toward an ecological psychology (pp. 62-82). Hillsdale, NJ: Lawrence Erlbaum Associates.

[24]

Gibson, J.J. (1986). The ecological approach to visual perception. Hillsdale, NJ: Lawrence Erlbaum.

[25]

Gilbert, E., & Karahalios, K (2010). Widespread worry and the stock market. In Proceedings of the Fourth International AAAI Conference on Web logs and Social Media. Menlo Park, CA: AAAI Press. Retriered November 5, 2010, from http://www.aaai.org/ocslindex.php/ICWSM/ICWSM10/ paper/view/1513/1833.

[26]

Gruhl, D., Chavet, L., Gibson, D., Meyer, J., & Pattanayak, P. (2004). How to build a WebFountain: An architecture for very large-scale text analytics. IBM Systems Journal, 43(1), 64-77.

Digital Library

[27]

Gruhl, D., Guha, R., Kumar, R., Novak, J., & Tomkins, A. (2005). The predictive power of online chatter. In R L. Grossman, R. Bayardo, K. Bennett & J. Vaidya (Eds.), Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (KDD '05) (pp. 78-87). New York: ACM Press.

[28]

Gruhl, D., Guha, R, Liben-Nowell, D., & Tomkins, A. (2004). Information diffusion through Blogspace. In Proceedings of the 13th International Conference on World Wide Web (WWW2004) (pp. 491-501). New York: ACM Press. Retrieved November 16, 2010, from http://people.csail.mit.edu/dln/papers/blogs/idib.pdf

[29]

Guralnik, V., & Srivastava, J. (J 999). Event detection from time series data. Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 33-42). New York: ACM Press.

[30]

Hamilton, J.D. (1994). Time series analysis. Princeton, NJ: University Press.

[31]

Honeycutt, C., & Herring, S.C. (2009). Beyond microblogging: Conversation and collaboration via Twitter. Proceedings of the 42nd Hawaii International Conference on System Sciences (HICSS '09) (pp. 1-10). Washington, DC: IEEE.

[32]

Hookway, N. (2008). Entering the 'blogosphere': Some strategies for using blogs in social research. Qualitative Research, 8(1), 91-113.

[33]

Huang, J., Thornton, KM., & Efthimiadis, E.N. (2010). Conversational tagging in twitter. Proceedings of the 21st ACM Conference on Hypertext and Hypermedia (pp. 173-178). New York: ACM Press.

[34]

Hughes, AL., & Palen, L. (2009). Twitter adoption and use in mass convergence and emergency events. International Journal of Emergency Management, 6(3-4), 248-260.

[35]

Jansen, B.J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169-2188.

[36]

Java, A, Song, X., Finin, T., & Tseng, B. (2007). Why we twitter: Understanding microblogging usage and communities. Proceedings of the Ninth WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis (pp. 56-65). New York: ACM Press.

[37]

Johnson, S. (2009). How Twitter will change the way we live. Retrieved November 5, 2010, from http://www.time.com/time/business/article/ 0,8599,1902604,00.html

[38]

Katz, E. (1959). Mass communication research and the study of popular culture. Studies in Public Communication, 2(1), 1-6.

[39]

Kerbel, M. (2000). If it bleeds, it leads An anatomy of television news. Boulder, CO: Westview Press.

[40]

Kinsinger, E.A., & Schacter, D.L. (2008). Memory and emotion. In M. Lewis, J.A. Haviland-Jones & L. Feldman Barrett (Eds.), Handbook of emotions Ord ed., pp. 601-617). New York: The Guildford Press.

[41]

Kramer, A.D.I. (2010). An unobtrusive behavioral model of "Gross National Happiness." In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2010), (pp. 287-290). New York: ACM Press.

[42]

Krishnamurthy, B., Gill, P., & Arlitt, M. (2008). A few chirps about twitter. In Proceedings of the First Workshop on Online Social Networks (pp. 19-24). New York: ACM Press.

[43]

Kumar, R, Novak, J., Raghavan, P., & Tomkins, A. (2003). On the bursty evolution of blogspace. In Proceedings of the 12th International World Wide Web Conference (WWW2003) (pp. 568-576). New York: ACM Press. Retrieved November 5, 2010, from http://www2003. org/cdrom/papers/refereed/p477/p4 77-kumar/p477-kumar.htm.

[44]

Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is Twitter, a social network or a news media? In Proceedings of the 19th International Conference on World Wide Web (WWW' 10) (pp. 591-600). New York: ACM Press.

[45]

Lenhart, A, Purcell, K, Smith, A., & Zickuhr, K (2010). Social media & mobile Internet use among teens and young adults. Pew Internet & American Life Project. Retrieved November 5, 2010, from http:// pewinternet.org/Reports/2010/Social-Media-and-Young-Adults.aspx.

[46]

MacLean, A., Carter, K, Lovstrand, L., & Moran, T. (1990). User-tailorable systems: Pressing the issues with buttons. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 175-182). New York: ACM Press.

[47]

Mishne, G., & Glance, N. (2006). Predicting movie sales from blogger sentiment. AAAI 2006 Spring Symposium on Computational Approaches to Analysing Weblogs, Retrieved November 5, 2010, from http://www. nielsen-online.com/downloads/us/buzz/wp_MovieSalesBlogSntmnt_ Glance_2005.pdf

[48]

Naaman, M., Boase, J., & Lai, C.-H. (2010). Is it really about me?: Message wntent in social awareness streams. In Proceedings of the 2010 ACM Conference on Computer Supported Cooperative Work (pp. 189-192). New York: ACM Press.

[49]

Nahl, D. (2006). Affective load. In K.E. Fisher, S. Erdelez, & L.E.F. McKenchnie (Eds.), Theories of information behavior (pp. 39-43). Medford, NJ: Information Today.

[50]

Nahl, D. (2007a). The centrality of the affective in information behavior. In D. Nahl & D. Bilal (Eds.), Information and emotion: The emergent affective paradigm in information behavior (pp. 3-37). Medford, NJ: Information Today.

[51]

Nahl, D. (2007b). Domain interaction discourse analysis: A technique for charting the flow of micro-information behavior. Journal of Documentation, 63(3), 323-339.

[52]

Neviarouskaya, A., Prendinger, H., & Ishizuka, M. (2007). Textual affect sensing for sociable and expressive online communication. Lecture Notes in Computer Science, 4738, 218-229.

Digital Library

[53]

O'Connor, B., Balasubramanyan, R., Routledge, B.R., & Smith, N.A. (2010). From tweets to polls: Linking text sentiment to public opinion time series. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (pp. 122-129). Menlo Park, CA: AAAI Press.

[54]

Pak, A, & Paroubek, P. (2010). Twitter as a corpus for sentiment analysis and opinion mining. In Proceedings of LREC 2010 (pp. 1320-1326). Paris: European Language Resource Association. Retrieved November 5, 2010, from http://www.lrec-conf.org/proceedings/lrec2010/pdf/385_Paper.pdf.

[55]

Pang, B., & Lee, L. (2004). Sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42nd Annual Association for Computational Linguistics Conference (pp. 271-278). Morristown, NJ: Association for Computational Linguistics.

[56]

Pang, B., & Lee, L. (2005). Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Association for Computational Linguistics Conference (pp. 115-124). Morristown, NJ: Association for Computational Linguistics.

[57]

Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 1(1-2), 1-135.

Digital Library

[58]

Pang, B., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp. 79-86). Morristown, NJ: Association for Computational Linguistics.

[59]

Perse, E.M. (1990). Involvement with local television news: Cognitive and emotional dimensions. Human Communication Research, 16(4), 556-581.

[60]

Porter, M. (1980). An algorithm for suffix stripping. Program, 14(3), 130-137.

[61]

Riloff, E., Patwardhan, S., & Wiebe, J. (2006). Feature subsumption for opinion anaJysis. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp. 440-448). Morristown, NJ: Association for Computational Linguistics.

[62]

Sakaki, T., Okazaki, M., & Matsuo, Y. (2010). Earthquake shakes Twitter users: Real-time event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web (WWW '10) (pp. 851- 860). New York: ACM Press.

[63]

Savage, M., & Burrows, R (2007). The coming crisis in empirical sociology. Sociology, 41(5), 885-899.

[64]

Seaton, J. (2005). Carnage and the media: The making and breaking of news about violence. London: Allen Lane.

[65]

Stafford, T.E., Stafford, M.R, & Schkade, L.L. (2004). Determining uses and gratifications for the Internet. Decision Sciences, 35(2), 259-288.

[66]

Strapparava, C., & Mihalcea, R. (2008). Learning to identify emotions in text. Proceedings of the 2008 ACM symposium on Applied computing (pp. 1556-1560). New York: ACM Press.

[67]

Swanson, D.R, Smalheiser, N.R, & Bookstein, A (2001). Information discovery from complementary literatures: Categorizing viruses as potential weapons. Journal of the American Society for Information Science and Technology, 52(10), 797-812.

Digital Library

[68]

Taboada, M., Brooke, J., Tofiloski, M., Voll, K, & Stede, M. (in press). Lexicon-based methods for sentiment anaJysis. Computational Lingui stics.

[69]

Tenopir, C., Nahl-Jakobovits, D., & Howard, D.L. (1991). Strategies and assessments online: Novices' experience. Library and Information Science Research, 13(3), 237-266.

[70]

Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., & Kappas, A. (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61(12), 2544-2558.

[71]

Thelwall, M., & Prabowo, R. (2007). Identifying and characterising public science-related concerns from RSS feeds. Journal of the American Society for Information Science and Technology, 58(3), 379-390.

Digital Library

[72]

Thelwall, M., Prabowo, R., & Fairclough, R. (2006). Are raw RSS feeds suitable for broad issue scanning? A science concern case study. Journal of the American Society for Information Science and Technology, 57(12), 1644-1654.

Digital Library

[73]

Thelwall, M., Wouters, P., & Fry, J. (2008). Information-centred research for large-scale analysis of new information sources. Journal of the American Society for Information Science and Technology, 59(9), 1523-1527.

Digital Library

[74]

Tumasjan, A., Sprenger, T.O., Sandner, P.G., & Welpe, I.M. (2010). Predicting elections with Twitter: What 140 characters reveal about political sentiment. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (pp. 178-185). Menlo Park, CA: The AAAI Press.

[75]

Wang, A.H. (2010, July). Don't follow me: Spam detection in Twetter. Paper presented at the Seventh Annual Collaboration, Electronic messaging, AntiAbuse and Spam Conference (CEAS), Redmond, WA. Retrieved October 5, 2010 from: http://ceas.cc/2010/papers/Paper%2021.pdf.

[76]

White, M. (2002). Representations or people" Ethics and Information Technology, 4(3), 249-266.

[77]

Wigand, F.D.L. (2010). Twitter in government: Building relatiolships one Tweet at a time. In Proceedings of the Seventh International Conference on Information Technology (pp. 563-567). Washington, DC: IEEE.

Digital Library

[78]

Wilson, T., Wiebe, J., & Hoffman, P. (2009). Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational Linguistics, 35(3), 399-433.

Digital Library

[79]

Wilson, T., Wiebe, J., & Hwa, R. (2006). Recognizing strong and weak opinion clauses. Computational Intelligence, 22(2), 73-99.

[80]

Witten, I.H., & Frank, E. (2005). Data mining: Practical machine learning tools and techniques. San Francisco: Morgan Kaufmann.

Cited By

Amiri MYaghtin MSotudeh H(2024)How do tweeters feel about scientific misinformation: an infoveillance sentiment analysis of tweets on retraction notices and retracted papersScientometrics10.1007/s11192-023-04871-7129:1(261-287)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11192-023-04871-7
Obreja D(2023)Mapping the Political Landscape on Social Media Using BibliometricsSocial Science Computer Review10.1177/0894439322111774941:5(1712-1728)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1177/08944393221117749
Cao YXia MShigyo KCheng FYu QYang XWang YZeng WQu H(2023)NFTeller: Dual-centric Visual Analytics for Assessing Market Performance of NFT CollectiblesProceedings of the 16th International Symposium on Visual Information Communication and Interaction10.1145/3615522.3615578(1-8)Online publication date: 22-Sep-2023
https://dl.acm.org/doi/10.1145/3615522.3615578
Show More Cited By

Recommendations

A sentiment analysis of audiences on twitter: who is the positive or negative audience of popular twitterers?
ICHIT'11: Proceedings of the 5th international conference on Convergence and hybrid information technology

Microblogging is a new informal communication medium of blogging that differs from a traditional blog in which content is much shorter. Microbloggers post about topics that describe their current status. Twitter is a popular microblogging service and ...
Topic sentiment analysis in twitter: a graph-based hashtag sentiment classification approach
CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

Twitter is one of the biggest platforms where massive instant messages (i.e. tweets) are published every day. Users tend to express their real feelings freely in Twitter, which makes it an ideal source for capturing the opinions towards various ...
Company event popularity for financial markets using Twitter and sentiment analysis

The work proposes an Event Popularity Algorithm for Financial Trading.The approach is based on sentiment analysis to the social network Twitter.Planning and performing a financial community for the extraction of analyzed tweets.The events are focused on ...

Comments

Information & Contributors

Information

Published In

cover image Journal of the American Society for Information Science and Technology

Journal of the American Society for Information Science and Technology Volume 62, Issue 2

February 2011

203 pages

ISSN:1532-2882

Issue’s Table of Contents

Publisher

John Wiley & Sons, Inc.

United States

Publication History

Published: 01 February 2011

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

120
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 10 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Amiri MYaghtin MSotudeh H(2024)How do tweeters feel about scientific misinformation: an infoveillance sentiment analysis of tweets on retraction notices and retracted papersScientometrics10.1007/s11192-023-04871-7129:1(261-287)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.1007/s11192-023-04871-7
Obreja D(2023)Mapping the Political Landscape on Social Media Using BibliometricsSocial Science Computer Review10.1177/0894439322111774941:5(1712-1728)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1177/08944393221117749
Cao YXia MShigyo KCheng FYu QYang XWang YZeng WQu H(2023)NFTeller: Dual-centric Visual Analytics for Assessing Market Performance of NFT CollectiblesProceedings of the 16th International Symposium on Visual Information Communication and Interaction10.1145/3615522.3615578(1-8)Online publication date: 22-Sep-2023
https://dl.acm.org/doi/10.1145/3615522.3615578
Jiang JThomason JBarbieri FFerrara EHorvát ÁHall WContractor NFröhling LOgnayova KTaneja HWeber IGligori? KMejova Y(2023)Geolocated Social Media Posts are Happier: Understanding the Characteristics of Check-in Posts on TwitterProceedings of the 15th ACM Web Science Conference 202310.1145/3578503.3583596(136-146)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3578503.3583596
Akritidis LBozanis P(2023)Low-Dimensional Text Representations for Sentiment Analysis NLP TasksSN Computer Science10.1007/s42979-023-01913-y4:5Online publication date: 23-Jun-2023
https://dl.acm.org/doi/10.1007/s42979-023-01913-y
Kim HMahmood MHwang HDawi N(2022)Applying Niche Theory to Measure Uses and Gratifications of Social Media in MalaysiaInternational Journal of Software Innovation10.4018/IJSI.28959410:2(1-11)Online publication date: 7-Jan-2022
https://dl.acm.org/doi/10.4018/IJSI.289594
Sumathy BKumar ASungeetha DHashmi ASaxena AKumar Shukla PNuagah S(2022)Machine Learning Technique to Detect and Classify Mental Illness on Social Media Using Lexicon-Based Recommender SystemComputational Intelligence and Neuroscience10.1155/2022/59067972022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/5906797
Ullah AKhan SNawi N(2022)Review on sentiment analysis for text classification techniques from 2010 to 2021Multimedia Tools and Applications10.1007/s11042-022-14112-382:6(8137-8193)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.1007/s11042-022-14112-3
Luo JXu WGao XChen G(2022)Which is better? A modularized evaluation for topic popularity predictionKnowledge and Information Systems10.1007/s10115-022-01733-164:11(3043-3080)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1007/s10115-022-01733-1
Husnain MMissen MAkhtar NCoustaty MMumtaz SPrasath V(2021)A systematic study on the role of SentiWordNet in opinion miningFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-019-9094-015:4Online publication date: 1-Aug-2021
https://dl.acm.org/doi/10.1007/s11704-019-9094-0
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents