Skip to main content
SpringerLink
Log in

Sentiment Analysis in Social Media

  • Chapter
  • First Online:
Online Collective Action

Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

Sentiment Analysis deals with the detection and analysis of affective content in written text. It utilizes methodologies, theories, and techniques from a diverse set of scientific domains, ranging from psychology and sociology to natural language processing and machine learning. In this chapter, we discuss the contributions of the field in social media analysis with a particular focus in online collective actions; as these actions are typically motivated and driven by intense emotional states (e.g., anger), sentiment analysis can provide unique insights into the inner workings of such phenomena throughout their life cycle. We also present the state of the art in the field and describe some of its contributions into understanding online collective behavior. Lastly, we discuss significant real-world datasets that have been successfully utilized in research and are available for scientific purposes and also present a diverse set of available tools for conducting sentiment analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 54.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://en.wikipedia.org/wiki/2011_England_riots

  2. 2.

    http://en.wikipedia.org/wiki/Arab_Spring

  3. 3.

    For more information about the terminology in the field, we refer the interested reader to chapter 1.5 of Pang and Lee (2008).

  4. 4.

    WordNet is a lexical database of English words which in addition to standard definitions also provides semantic relations between words.

  5. 5.

    http://www.livejournal.com

  6. 6.

    https://twitter.com/tos

  7. 7.

    API stands for “Application Protocol Interface” and usually provides methods for accessing the content of services or software through programming techniques. A guide to the Twitter API can be found here: https://dev.twitter.com/

  8. 8.

    http://code.google.com/apis/youtube/overview.html

  9. 9.

    Available at: http://www.icwsm.org/2009/data/index.shtml

  10. 10.

    Available at: http://icwsm.org/data/index.php

  11. 11.

    http://incubator.apache.org/opennlp/

  12. 12.

    http://alias-i.com/lingpipe/web/models.html

  13. 13.

    Available at: http://www.cyberemotions.eu/data.html

  14. 14.

    Available at: http://code.google.com/p/opinionfinder/

References

  • Agarwal A, Xie B, Vovsha I, Rambow O, Passonneau R (2011) Sentiment analysis of Twitter data. In: Proceedings of LSM’11. Association for Computational Linguistics, Stroudsburg, PA, pp 30–38

    Google Scholar 

  • Alias-i (2008) Lingpipe 4.1.0. http://alias-i.com/lingpipe

  • Baccianella S, Esuli A, Sebastiani F (2010) Sentiwordnet 3.0. In: Proceedings of LREC’10, Valletta, Malta

    Google Scholar 

  • Barnett E (2009) Facebook fuelling divorce, research claims (21 Dec, 2009, The Telegraph). http://www.telegraph.co.uk/technology/facebook/6857918/Facebook-fuelling-divorce-research-claims.html. Accessed 08 Sept 2011

  • Barrett LF, Russell JA (1999) The structure of current affect: controversies and emerging consensus. Curr Dir Psychol Sci 8(1)

    Google Scholar 

  • Bautin M, Vijayarenu L, Skiena S (2008) International sentiment analysis for news and blogs. In: Proceedings of ICWSM’08, Seattle, Washington, DC

    Google Scholar 

  • Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer, New York

    Google Scholar 

  • Bollen J, Mao H, Zeng XJ (2010) Twitter mood predicts the stock market. CoRR. http://arxiv.org/abs/1010.3003

  • Bollen J, Mao H, Pepe A (2011) Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In ICWSM, Barcelona, Spain

    Google Scholar 

  • Bradley MM, Lang PJ (1999) Affective norms for English words (ANEW): instruction manual and affective ratings. The Center for Research in Psychophysiology, Gainesville, FL

    Google Scholar 

  • Brew A, Greene D, Cunningham P (2010) Using crowdsourcing and active learning to track sentiment in online media. In: Proceedings of ECAI’10, Lisbon, Portugal, pp 145–150

    Google Scholar 

  • Burton KNK, Soboroff I (2011) The ICWSM 2011 spinn3r dataset. ICWSM, Barcelona

    Google Scholar 

  • Burton K, Java A, Soboroff I (2009) The ICWSM 2009 spinn3r dataset. ICWSM, San Jose, CA

    Google Scholar 

  • Cha M, Pérez JAN, Haddadi H (2009) Flash floods and ripples: the spread of media content through the blogosphere. In: Proceedings of ICWSM’11, Barcelona, Spain

    Google Scholar 

  • Chang CC, Lin CJ (2011) Libsvm: a library for support vector machines. ACM Trans Intell Syst Technol 2:27.1–27.27

    Google Scholar 

  • Chang R, Pimentel S, Svistunov A (2011) Sentiment analysis of occupy wall street tweets. http://cs229.stanford.edu/proj2011/ChangPimentelSvistunov-SentimentAnalysisOfOccupyWallStreetTweets.pdf

  • Chen H, Zimbra D (2010) Ai and opinion mining. IEEE Intell Syst 25:74–80

    Google Scholar 

  • Chmiel A, Sienkiewicz J, Thelwall M, Paltoglou G, Buckley K, Kappas A (2011a) Collective emotions online and their influence on community life. PLoS ONE 6(7):e22207

    Google Scholar 

  • Chmiel A, Sobkowicz P, Sienkiewicz J, Paltoglou G, Buckley K, Thelwall M (2011b) Negative emotions boost user activity at BBC forum. Phys A 390(16):2936–2944

    Article  Google Scholar 

  • Dalgleish T, Power M (1999) Handbook of cognition and emotion. Wiley, New York

    Book  Google Scholar 

  • Diakopoulos NA, Shamma DA (2010) Characterizing debate performance via aggregated Twitter sentiment. In: Proceedings of CHI’10, Atlanta, GA, pp 1195–1198

    Google Scholar 

  • Ding X, Liu B, Yu PS (2008) A holistic lexicon-based approach to opinion mining. In: Proceedings of WSDM’08, Palo Alto, CA, pp 231–240

    Google Scholar 

  • Dodds P, Danforth C (2009) Measuring the happiness of large-scale written expression: songs, blogs, and presidents. J Happiness Stud. doi:10.1007/s10902-009-9150-9

    Google Scholar 

  • Fan RE, Chang KW, Hsieh CJ, Wang XR, Lin CJ (2008) LIBLINEAR: a library for large linear classification. J Mach Learn Res 9:1871–1874

    MATH  Google Scholar 

  • Fouche G (2011) Nobel peace prize may recognise Arab spring. (28 Sept, 2011, Reuters). http://in.reuters.com/article/2011/09/27/idINIndia-59582020110927. Accessed 27 Dec 2011

  • Gazzar SE, Vitorovich L, Bender R (2011) Egypt communications cut ahead of further protests (28 Jan 2011, The Wall Street Journal). http://online.wsj.com/article/BT-CO-20110128-706943.html. Accessed 27 Dec 2011

  • Godbole N, Srinivasaiah M, Skiena S (2007) Large-scale sentiment analysis for news and blogs. In: Proceedings of ICWSM’07, Boulder, CO

    Google Scholar 

  • Gonzalez-Bailon S, Paltoglou G (2012) The positive effects of negative emotions in online communities (under review)

    Google Scholar 

  • Gonzalez-Bailon S, Banchs RE, Kaltenbrunner A (2010) Emotional reactions and the pulse of public opinion: Measuring the impact of political events on the sentiment of online discussions. CoRR. http://arxiv.org/abs/1009.4019

  • Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. SIGKDD Explor Newsl 11:10–18

    Article  Google Scholar 

  • Halliday J, Garside J (2011) Rioting leads to cameron call for social media clampdown (11 Aug, 2011, The Guardian). http://www.guardian.co.uk/uk/2011/aug/11/cameron-call-social-media-clampdown. Accessed 27 Dec 2011

  • Harvey M (2010) Facebook ousts Google in us popularity (17 Mar 2010, The Sunday Times). http://technology.timeson-line.co.uk/tol/news/tech_and_web/the_web/article7064973.ece. Accessed 05 July 2010

  • Howard P (2011) The Arab spring’s cascading effects (23 Feb 2011, Miller-McCune). http://www.miller-mccune.com/politics/the-cascading-effects-of-the-arab-spring-28575/. Accessed 27 Dec 2011

  • Jijkoun V, de Rijke M, Weerkamp W (2010) Generating focused topic-specific sentiment lexicons. In: Proceedings of ACL ’08, Columbus, OH, pp 585–594

    Google Scholar 

  • Joachims T (1999) Making large-scale SVM learning practical. In: Advances in kernel methods - support vector learning, vol 11. MIT Press, Cambridge, MA

    Google Scholar 

  • John GH, Langley P (1995) Estimating continuous distributions in Bayesian classifiers. Stanford University, Stanford, CA, pp 338–345

    Google Scholar 

  • Kramer AD (2010) An unobtrusive behavioral model of “gross national happiness”. In: Proceedings of CHI’10, Atlanta, GA, pp 287–290

    Google Scholar 

  • Le Cessie S, Van Houwelingen JC (1992) Ridge estimators in logistic regression. Appl Stat 41(1):191–201

    Article  MATH  Google Scholar 

  • Manning CD, Schütze H (1999) Foundations of statistical natural language processing. MIT Press, Cambridge, MA

    MATH  Google Scholar 

  • Mauss IB, Robinson MD (2009) Measures of emotion: a review. Cogn Emotion 23(2):209–237

    Article  Google Scholar 

  • McCallum AK (2002) Mallet: a machine learning for language toolkit. http://www.cs.umass.edu/mccallum/mallet

  • Miller GA (1995) Wordnet: a lexical database for English. Commun ACM 38(11):39–41

    Article  Google Scholar 

  • Mishne G (2005) Experiments with mood classification in blog posts. In: 1st Workshop on stylistic analysis of text for information access, Salvador, Brazil

    Google Scholar 

  • Mishne G, de Rijke M (2006) Capturing global mood levels using blog posts. In: Proceedings of AAAI-CAAW, Stanford University, Stanford, CA, pp 145–152

    Google Scholar 

  • Mitchell TM (1997) Machine learning, 1st edn. McGraw-Hill, New York

    Google Scholar 

  • Mitrović M, Paltoglou G, Tadić B (2011) Quantitative analysis of bloggers’ collective behavior powered by emotions. J Stat Mech Theory Exp 2011(02):P02005

    Google Scholar 

  • O’Connor B, Balasubramanyan R, Routledge BR, Smith NA (2010) From tweets to polls: linking text sentiment to public opinion time series. In: Proceedings of ICWSM’10, Washington, DC

    Google Scholar 

  • Owsley S, Sood S, Hammond KJ (2006) Domain specific affective classification of documents. In: Proceedings of AAAICAAW’06, Stanford University, Stanford, CA, pp 181–183

    Google Scholar 

  • Pak A, Paroubek P (2010) Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of LREC’10, Valletta, Malta

    Google Scholar 

  • Paltoglou G, Thelwall M (2012) Twitter, myspace, digg: unsupervised sentiment analysis in social media. ACM TIST 3(4):66.1–66.19

    Google Scholar 

  • Paltoglou G, Thelwall M, Buckely K (2010) Online textual communication annotated with grades of emotion strength. In: Proceedings of EMOTION, Imperial College, London, pp 25–31

    Google Scholar 

  • Paltoglou G, Theunis M, Kappas A, Thelwall M (2013) Predicting emotional responses to long informal text. J IEEE Trans Affect Comput 99(PrePrints):1

    Google Scholar 

  • Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends Inform Retrieval 2(1–2):1–135

    Article  Google Scholar 

  • Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of EMNLP’02. Association for Computational Linguistics, Philadelphia, pp 79–86

    Google Scholar 

  • Pennebaker JW (2008) Debate 3: Mccain and Obama word usage (15 Oct 2008, WordWathcers). http://wordwatchers.wordpress.com/2008/10/15/debate-3-mccain-and-obama-word-usage/. Accessed 01 Feb 2012

  • Pennebaker JW, Francis ME (1999) Linguistic inquiry and word count, 1st edn. Lawrence Erlbaum, Mahwah, NJ

    Google Scholar 

  • Pennebaker JW, Persaud R (2010) The 2010 UK election: the second debate (23 Apr 2010, WordWathcers). http://wordwatchers.wordpress.com/2010/04/23/the-2010-uk-election-the-second-debate/. Accessed 01 Feb 2012

  • Platt, J. C. (1999). Fast training of support vector machines using sequential minimal optimization. John C. Platt Microsoft Research 1 Microsoft Way, Redmond, WA, pp 185–208

    Google Scholar 

  • Quirk R, Greenbaum S, Leech G, Svartvik J (1985) A comprehensive grammar of the English language. Longman, London

    Google Scholar 

  • Russell JA (1980) A circumplex model of affect. J Person Soc Psychol 39(6):1161–1178

    Article  Google Scholar 

  • Sebastiani F (2002) Machine learning in automated text categorization. ACM Comput Surv 34(1):1–47

    Article  Google Scholar 

  • Strapparava C, Mihalcea R (2007) Semeval-2007 task 14: affective text. In: Proceedings of SemEval’07, Prague, Czech Republic, pp 70–74

    Google Scholar 

  • Strapparava C, Mihalcea R (2008) Learning to identify emotions in text. In: Proceedings of SAC’08, Fortaleza, Ceara, Brazil, pp 1556–1560

    Google Scholar 

  • Strapparava C, Valitutti A (2004) WordNet-Affect: an affective extension of WordNet. In: Proceedings of LREC’04 (Vol 4), Lisbon, Portugal, pp 1083–1086

    Google Scholar 

  • Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2001) Lexicon-based methods for sentiment analysis. Comput Linguistics 37:267–307

    Article  Google Scholar 

  • Thelwall M (2009) Myspace comments. Online Inform Rev 33(1):58–76

    Article  Google Scholar 

  • Thelwall M, Buckley K, Paltoglou G, Di C, Kappas A (2010) Sentiment strength detection in short informal text. JASIST 61(12):2544–2558

    Article  Google Scholar 

  • Thelwall M, Buckley K, Paltoglou G (2011) Sentiment in Twitter events. J Am Soc Inf Sci Technol 62:406–418

    Article  Google Scholar 

  • Thomas M, Pang B, Lee L (2006) Get out the vote: determining support or opposition from congressional floor-debate transcripts. In: Proceedings of EMNLP’06. Association for Computational Linguistics, Morristown, pp 327–335

    Google Scholar 

  • Tonkin E, Pfeiffer HD, Tourte G (2012) Twitter, information sharing and the London riots. Bull Am Soc Inf Sci Tech 38(2):49–57

    Article  Google Scholar 

  • Vapnik VN (1999) The nature of statistical learning theory (information science and statistics). Springer, Heidelberg

    Google Scholar 

  • Velikovich L, Blair-Goldensohn S, Hannan K, McDonald R (2010) The viability of web-derived polarity lexicons. In: Proceedings of HLT’10, Stroudsburg, PA, USA, pp 777–785

    Google Scholar 

  • Whitelaw C, Garg N, Argamon S (2005) Using appraisal groups for sentiment analysis. In: Proceedings of CIKM’05, Bremen, Germany, pp 625–631

    Google Scholar 

  • Wiebe J, Bruce RF, O’Hara TP (1999) Development and use of a gold-standard data set for subjectivity classifications. In: Proceedings of 37th annual meeting of ACL, College Park, MD, USA, pp 246–253

    Google Scholar 

  • Wilson T, Hoffmann P, Somasundaran S, Kessler J, Wiebe J, Choi Y (2005a) Opinionfinder: a system for subjectivity analysis. In: Proceedings of HLT/EMNLP-demo, Vancouver, BC, Canada, pp 34–35

    Google Scholar 

  • Wilson T, Wiebe J, Hoffmann P (2005b) Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of EMNLP’05, Vancouver, BC, Canada, pp 347–354

    Google Scholar 

  • Zaidan O, Eisner J, Piatko CD (2007) Using “annotator rationales” to improve machine learning for text categorization. In: Proceedings of HLT-NAACL. Association of Computational Linguistics, Rochester, NY, pp 260–267

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Technology, University of Wolverhampton, Wulfruna Street, Wolverhampton, WV1 1LY, UK

    Georgios Paltoglou

Authors
  1. Georgios Paltoglou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Georgios Paltoglou .

Editor information

Editors and Affiliations

  1. Department of Information Science, University of Arkansas at Little Rock, Little Rock, Arkansas, USA

    Nitin Agarwal

  2. School of Journalism and Communication, Carleton University, Ottawa, Ontario, Canada

    Merlyna Lim

  3. Departments of Information Science and Business Information Systems, University of Arkansas at Little Rock, Little Rock, USA

    Rolf T. Wigand

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Wien

About this chapter

Cite this chapter

Paltoglou, G. (2014). Sentiment Analysis in Social Media. In: Agarwal, N., Lim, M., Wigand, R. (eds) Online Collective Action. Lecture Notes in Social Networks. Springer, Vienna. https://doi.org/10.1007/978-3-7091-1340-0_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-7091-1340-0_1

  • Published:

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-7091-1339-4

  • Online ISBN: 978-3-7091-1340-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation