Hostname: page-component-7b9c58cd5d-7g5wt Total loading time: 0 Render date: 2025-03-14T13:44:09.170Z Has data issue: false hasContentIssue false
  • English
  • Français

Sentiment analysis in Twitter

Published online by Cambridge University Press:  27 November 2012

EUGENIO MARTÍNEZ-CÁMARA ,
M. TERESA MARTÍN-VALDIVIA ,
L. ALFONSO UREÑA-LÓPEZ  and
A RTURO MONTEJO-RÁEZ
EUGENIO MARTÍNEZ-CÁMARA
Affiliation:
Computer Science Department, University of Jaén, Campus Las Lagunillas, 23071 Jaén, Spain email: emcamara@ujaen.es, maite@ujaen.es, laurena@ujaen.es, amontejo@ujaen.es
M. TERESA MARTÍN-VALDIVIA
Affiliation:
Computer Science Department, University of Jaén, Campus Las Lagunillas, 23071 Jaén, Spain email: emcamara@ujaen.es, maite@ujaen.es, laurena@ujaen.es, amontejo@ujaen.es
L. ALFONSO UREÑA-LÓPEZ
Affiliation:
Computer Science Department, University of Jaén, Campus Las Lagunillas, 23071 Jaén, Spain email: emcamara@ujaen.es, maite@ujaen.es, laurena@ujaen.es, amontejo@ujaen.es
A RTURO MONTEJO-RÁEZ
Affiliation:
Computer Science Department, University of Jaén, Campus Las Lagunillas, 23071 Jaén, Spain email: emcamara@ujaen.es, maite@ujaen.es, laurena@ujaen.es, amontejo@ujaen.es
Get access Rights & Permissions [Opens in a new window]

Abstract

In recent years, the interest among the research community in sentiment analysis (SA) has grown exponentially. It is only necessary to see the number of scientific publications and forums or related conferences to understand that this is a field with great prospects for the future. On the other hand, the Twitter boom has boosted investigation in this area due fundamentally to its potential applications in areas such as business or government intelligence, recommender systems, graphical interfaces and virtual assistance. However, to fully understand this issue, a profound revision of the state of the art is first necessary. It is for this reason that this paper aims to represent a starting point for those investigations concerned with the latest references to Twitter in SA.

Type
Articles
Information
Natural Language Engineering , Volume 20 , Issue 1 , January 2014 , pp. 1 - 28
Copyright
Copyright © Cambridge University Press 2012 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Abel, F., Celik, I. and Siehndel, P. 2011. Towards a framework for adaptive faceted search on Twitter. In Proceedings of the International Workshop on Dynamic and Adaptive Hypertext (DAH), in conjunction with ACM Hypertext, Eindhoven, The Netherlands.Google Scholar
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., and Passonneau, R. 2011 (June). Sentiment analysis of Twitter data. In Proceedings of the Workshop on Language in Social Media (LSM 2011), Portland, Oregon, pp. 30–8. Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
Aisopos, F., Papadakis, G., Tserpes, K. and Varvarigou, T. 2012. Content vs. context for sentiment analysis: a comparative analysis over microblogs. In Proceedings of the 23rd ACM Conference on Hypertext and Social Media, pp. 187–96. New York: ACM.Google Scholar
Asur, S. and Huberman, B. A. 2010. Predicting the future with social media. In Web Intelligence and Intelligent Agent Technology, IEEE/WIC/ACM International Conference on 1: 492–9.Google Scholar
Barbosa, L. and Feng, J. 2010. Robust sentiment detection on Twitter from biased and noisy data. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters (COLING ‘10), pp. 36–44. Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
Bermingham, A. and Smeaton, A. 2011 (November). On using Twitter to monitor political sentiment and predict election results. In Proceedings of the Workshop on Sentiment Analysis Where AI Meets Psychology (SAAIP 2011), pp. 2–10. Chiang Mai, Thailand: Asian Federation of Natural Language Processing.Google Scholar
Bifet, A. and Frank, E. 2010. Sentiment knowledge discovery in Twitter streaming data. In Proceedings of the 13th International Conference on Discovery Science, pp. 1–15. Berlin: Springer.Google Scholar
Bifet, A., Holmes, G., Kirkby, R. and Pfahringer, B. 2010. MOA: massive online analysis. Journal of Machine Learning Research 11: 1601–1604.Google Scholar
Bollen, J., Mao, H. and Zeng, X.-J. 2011. Twitter mood predicts the stock market. Journal of Computational Science 2 (1): 1–8.CrossRefGoogle Scholar
Bollen, J., Pepe, A. and Mao, H. 2011. Modeling public mood and emotion: Twitter sentiment and socioeconomic phenomena. In Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media. Barcelona, Spain.Google Scholar
Bradley, M. M. and Lang, P. J. 1999. Affective norms for English words (ANEW): stimuli, instruction manual, and affective ratings. Technical Report, Center for Research in Psychophysiology, University of Florida.Google Scholar
Castillo, C., Mendoza, M. and Poblete, B. 2011. Information credibility on Twitter. In Proceedings of the 20th International Conference on World Wide Web, pp. 675–84. New York: ACM.CrossRefGoogle Scholar
Claster, W. B., Dinh, H. and Cooper, M. 2010. Naïve Bayes and unsupervised artificial neural nets for Cancun tourism social media data analysis. In Nature and Biologically Inspired Computing (NaBIC), 2010 Second World Congress, p. 158. Kitakyushu, JapanCrossRefGoogle Scholar
Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20 (1): 37–46.CrossRefGoogle Scholar
Cotelo, J. M., Cruz, F. L. and Troyano, J. A. 2012. Generación adaptativa de consultas para la recuperación temática de tweets. Procesamiento de Lenguaje Natural 48: 57–64.Google Scholar
Danescu-Niculescu-Mizil, C., Gamon, M. and Dumais, S. 2011. Mark my words!: linguistic style accommodation in social media. In Proceedings of the 20th International Conference on World Wide Web, pp. 745–54. New York: ACM.CrossRefGoogle Scholar
Davidov, D., Tsur, O. and Rappoport, A. 2010a. Enhanced sentiment learning using Twitter hashtags and smileys. In Proceedings of the 23rd International Conference on Computational Linguistics: Posters, COLING ‘10, pp. 241–9. Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
Davidov, D., Tsur, O. and Rappoport, A. 2010b. Semi-supervised recognition of sarcastic sentences in Twitter and Amazon. In Proceedings of the Fourteenth Conference on Computational Natural Language Learning, pp. 107–16. Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
Efron, M. 2011. Information search and retrieval in microblogs. Journal of the American Society for Information Science and Technology 62 (6): 996–1008.CrossRefGoogle Scholar
Gayo-Avello, D. 2010. Nepotistic relationships in Twitter and their impact on rank prestige algorithms. preprint (arXiv:1004.0816).Google Scholar
Gayo-Avello, D. 2012. “I wanted to predict elections with Twitter and all I got was this lousy paper" – a balanced survey on election prediction using Twitter data. preprint (arXiv:1204.6441).CrossRefGoogle Scholar
Gibbs, R. W. 1986. On the psycholinguistics of sarcasm. Journal of Experimental Psychology 115 1: 3–15.CrossRefGoogle Scholar
Go, A., Bhayani, R. and Huang, L. 2009. Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford.Google Scholar
González-Ibáñez, R., Muresan, S., and Wacholder, N. 2011. Identifying sarcasm in Twitter: a closer look. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Portland, Oregon. Citeseer.Google Scholar
Hernández, S., and Sallis, P. 2011. Sentiment-preserving reduction for social media analysis. In Martin, C. San and Kim, S.-W. (eds.), Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Lecture Notes in Computer Science, Vol. 7042, pp. 409–16. Berlin/Heidelberg: Springer.CrossRefGoogle Scholar
Horrigan, J. A. 2008. Online shopping. Technical report. Pew Internet & American Life Project Report.Google Scholar
Jansen, B. J., Zhang, M., Sobel, K. and Chowdury, A. 2009. Micro-blogging as online word of mouth branding. In Proceedings of the 27th International Conference Extended Abstracts on Human Factors in Computing Systems, pp. 3859–64. New York: ACM.Google Scholar
Java, A., Song, X., Finin, T. and Tseng, B. 2007. Why we twitter: understanding microblogging usage and communities. In Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pp. 56–65. New York: ACM.CrossRefGoogle Scholar
Jiang, L., Yu, M., Zhou, M., Liu, X., and Zhao, T. 2011. Target-dependent twitter sentiment classification. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Vol. 1, pp. 151–60. Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
Jungherr, A., Jürgens, P., and Schoen, H. 2012. Why the Pirate Party won the German Election of 2009 or the trouble with predictions: a response to Tumasjan, A., Sprenger, T. O., Sander, P. G., and Welpe, I. M. ‘Predicting elections with Twitter: what 140 characters reveal about political sentiment’. Social Science Computer Review 30 2: 229–34.CrossRefGoogle Scholar
Jurgens, D. 2011. Word sense induction by community detection. In Proceedings of TextGraphs-6: Graph-based Methods for Natural Language Processing, pp. 24–28. Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
Kim, E., Gilbert, S., Edwards, M. J. and Graeff, E. 2009. Detecting sadness in 140 characters: sentiment analysis and mourning Michael Jackson on Twitter. Web Ecology 03(August).Google Scholar
Kivran-Swaine, F., Govindan, P. and Naaman, M. 2011. The impact of network structure on breaking ties in online social networks: unfollowing on Twitter. In Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems, pp. 1101–4. New York: ACM.Google Scholar
Kreuz, R. and Glucksberg, S. 1989. How to be sarcastic: the echoic reminder theory of verbal irony. Journal of Experimental Psychology: General 118 4: 374–86.CrossRefGoogle Scholar
Krishnamurthy, B., Gill, P. and Arlitt, M. 2008. A few chirps about Twitter. In Proceedings of the First Workshop on Online Social Networks, pp. 19–24. New York: ACM.CrossRefGoogle Scholar
Lin, C. and He, Y. 2009. Joint sentiment/topic model for sentiment analysis. In Proceedings of the 18th ACM conference on Information and Knowledge Management, pp. 375–84. New York: ACM.CrossRefGoogle Scholar
Liu, B. 2010. Sentiment analysis and subjectivity. In Indurkhya, Nitin and Damerau, Fred J. (eds.), Handbook of Natural Language Processing, 2nd ed., pp. 629–666.Google Scholar
Marcus, A., Bernstein, M. S., Badar, O., Karger, D. R., Madden, S. and Miller, R. C. 2011a. Twitinfo: aggregating and visualizing microblogs for event exploration. In Proceedings of the 2011 Annual Conference on Human Factors in Computing Systems, pp. 227–36. New York: ACM.Google Scholar
Marcus, A., Bernstein, M. S., Badar, O., Karger, D. R., Madden, S. and Miller, R. C. 2011b. Tweets as data: demonstration of tweeql and twitinfo. In Proceedings of the 2011 International Conference on Management of Data, pp. 1259–62. New York: ACM.Google Scholar
Maynard, D. and Funk, A. 2012. Automatic detection of political opinions in tweets. In García-Castro, R., Fensel, D., and Antoniou, G. (eds.), The Semantic Web: ESWC 2011 Workshops, Lecture Notes in Computer Science, Vol. 7117, pp. 88–99. Berlin/Heidelberg: Springer.CrossRefGoogle Scholar
Maynard, D., Tablan, V., Cunningham, H., Ursu, C., Saggion, H., Bontcheva, K., and Wilks, Y. 2002. Architectural elements of language engineering robustness. Natural Language Engineering 8 3: 257–74.CrossRefGoogle Scholar
McNair, D. M., Lorr, M. and Droppleman, L. F. 1971. Profile of mood states (POMS). San Diego: Educational and Industrial Testing Service.Google Scholar
Morris, M. R., Counts, S., Roseway, A., Hoff, A., and Schwarz, J. 2012. Tweeting is believing? understanding microblog credibility perceptions. In Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work, pp. 441–50. New York: ACM.CrossRefGoogle Scholar
O'Connor, B., Balasubramanyan, R., Routledge, B., and Smith, N. 2010. From tweets to polls: linking text sentiment to public opinion time series. In Proceedings of the International AAAI Conference on Weblogs and Social Media, pp. 122–9.Google Scholar
Pak, A. and Paroubek, P. 2010 (May). Twitter as a corpus for sentiment analysis and opinion mining. In Chair, N. C. C., Choukri, K., Maegaard, B., Mariani, J., Odijk, J., Piperidis, S., Rosner, M. and Tapias, D. (eds.), Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC‘10), Valletta, Malta; ELRA, pp. 19–21. European Language Resources Association.Google Scholar
Pang, B. and Lee, L. 2008 (January). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2 (1–2): 1–135.CrossRefGoogle Scholar
Pang, B., Lee, L. and Vaithyanathan, S. 2002. Thumbs up? sentiment classification using machine learning techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86.Google Scholar
Pepe, A. and Bollen, J. 2008. Between conjecture and memento: shaping a collective emotional perception of the future. In Proceedings of the AAAI Spring Symposium on Emotion, Personality, and Social Behavior. ArXiv: abs/0801.3864, pp. 111–116. Palo Alto, CA, AAAI.Google Scholar
Petrović, S., Osborne, M. and Lavrenko, V. 2010. The Edinburgh Twitter Corpus. In Proceedings of the NAACL HLT 2010 Workshop on Computational Linguistics in a World of Social Media, pp. 25–26. Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
Prabowo, R. and Thelwall, M. 2009. Sentiment analysis: a combined approach. Journal of Informetrics 3 2: 143–57.CrossRefGoogle Scholar
Read, J. 2005. Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In Proceedings of the ACL Student Research Workshop (ACLstudent ‘05), pp. 43–8. Stroudsburg, PA: Association for Computational Linguistics.CrossRefGoogle Scholar
Reyes, A., Rosso, P. and Buscaldi, D. 2012. From humor recognition to irony detection: the figurative language of social media. Data and Knowledge Engineering 74: 1–12.CrossRefGoogle Scholar
Romero, D. M., Meeder, B. and Kleinberg, J. 2011. Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on Twitter. In Proceedings of the 20th International Conference on World Wide Web (WWW ‘11), pp. 695–704. New York: ACM.CrossRefGoogle Scholar
Saif, H., He, Y. and Alani, H. 2012. Alleviating data sparsity for Twitter sentiment analysis. In Making Sense of Microposts (#MSM2012), pp. 2–9. Lyon, France.Google Scholar
Speriosu, M., Sudan, N., Upadhyay, S. and Baldridge, J. 2011. Twitter polarity classification with label propagation over lexical links and the follower graph. In Proceedings of the First Workshop on Unsupervised Learning in NLP (EMNLP‘11), pp. 53–63. Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
Thelwall, M., Buckley, K. and Paltoglou, G. 2011. Sentiment in Twitter events. Journal of the American Society for Information Science and Technology 62 2: 406–18.CrossRefGoogle Scholar
Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., and Kappas, A. 2010. Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology 61 12: 2544–58.CrossRefGoogle Scholar
Tsur, O., Davidov, D. and Rappoport, A. 2010. ICWSM: a great catchy name: semi-supervised recognition of sarcastic sentences in online product reviews. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, pp. 162–9.Google Scholar
Tsytsarau, M. and Palpanas, T. 2012. Survey on mining subjective data on the web. Data Mining and Knowledge Discovery 24: 478–514.CrossRefGoogle Scholar
Tumasjan, A., Sprenger, T., Sandner, P. and Welpe, I. 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. 178–85.Google Scholar
Tumasjan, A., Sprenger, T. O., Sandner, P. G. and Welpe, I. M. 2012 (May). Where there is a sea there are pirates: response to Jungherr, Jürgens, and Schoen. Social Science Computer Review 30 2: 235–9.CrossRefGoogle Scholar
Tunkelang, D. 2009. A Twitter analog to pagerank. http://thenoisychannel.com/2009/01/13/a-twitter-analog-to-pagerank/Google Scholar
Turney, P. D. 2002. Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics (ACL ‘02), pp. 417–24. Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
Whissell, C. M. 1989. The Dictionary of Affect in Language, Vol. 4; Chapter: Emotion theory research and experience, pp. 113–31. New York: Academic Press.Google Scholar
Zhang, L., Ghosh, R., Dekhil, M., Hsu, M., and Liu, B. 2011. Combining lexicon-based and learning-based methods for Twitter sentiment analysis. Technical Report HPL-2011-89.Google Scholar