Sentiment analysis in Twitter

Natural Language Engineering

Volumn 20, Issue 1, 2014, Pages 1-28

  Martínez Cámara, Eugenio ^a   Martín Valdivia, M Teresa ^a   Ureña López, L Alfonso ^a   Montejo Ráez, A Rturo ^a  

^a UNIVERSITY OF JAÉN   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

GRAPHICAL INTERFACE; RESEARCH COMMUNITIES; SCIENTIFIC PUBLICATIONS; SENTIMENT ANALYSIS; STATE OF THE ART;

DATA MINING;

SOCIAL NETWORKING (ONLINE);

EID: 84889850766     PISSN: 13513249     EISSN: 14698110     Source Type: Journal    
DOI: 10.1017/S1351324912000332     Document Type: Article

References (65)

1. 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.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. 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.
8. Bifet, A., Holmes, G., Kirkby, R., and Pfahringer, B. 2010. MOA: massive online analysis. Journal of Machine Learning Research 11: 1601-1604.
9. Bollen, J., Mao, H., and Zeng, X.-J. 2011. Twitter mood predicts the stock market. Journal of Computational Science 2(1): 1-8.
10. 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.
11. 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.
12. 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.
13. Claster, W. B., Dinh, H., and Cooper, M. 2010. Naive 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, Japan
14. Cohen, J. 1960. A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1): 37-46.
15. Cotelo, J. M., Cruz, F. L., and Troyano, J. A. 2012. Generació n adaptativa de consultas para la recuperació n temá tica de tweets. Procesamiento de Lenguaje Natural 48: 57-64.
16. 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.
17. 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.
18. 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.
19. Efron, M. 2011. Information search and retrieval in microblogs. Journal of the American Society for Information Science and Technology 62(6): 996-1008.
20. Gayo-Avello, D. 2010. Nepotistic relationships in Twitter and their impact on rank prestige algorithms. preprint (arXiv:1004.0816).
21. 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).
22. Gibbs, R. W. 1986. On the psycholinguistics of sarcasm. Journal of Experimental Psychology 115(1): 3-15.
23. Go, A., Bhayani, R., and Huang, L. 2009. Twitter sentiment classification using distant supervision. CS224N Project Report, Stanford.
24. Gonźalez-Ibanez, 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.
25. Herńandez, S., and Sallis, P. 2011. Sentiment-preserving reduction for social media analysis. In C. San Martin and S.-W. Kim (eds.), Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Lecture Notes in Computer Science, Vol. 7042, pp. 409- 16. Berlin/Heidelberg: Springer.
26. Horrigan, J. A. 2008. Online shopping. Technical report. Pew Internet & American Life Project Report.
27. 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.
28. 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.
29. 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.
30. Jungherr, A., Jurgens, 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.
31. 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.
32. 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).
33. 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.
34. 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.
35. 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.
36. 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.
37. Liu, B. 2010. Sentiment analysis and subjectivity. In Nitin Indurkhya and Fred J. Damerau (eds.), Handbook of Natural Language Processing, 2nd ed., pp. 629-666.
38. 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.
39. 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.
40. Maynard, D., and Funk, A. 2012. Automatic detection of political opinions in tweets. In R. Garc'ia-Castro, D. Fensel, and Antoniou, G. (eds.), The Semantic Web: ESWC 2011 Workshops, Lecture Notes in Computer Science, Vol. 7117, pp. 88-99. Berlin/Heidelberg: Springer.
41. 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.
42. McNair, D. M., Lorr, M., and Droppleman, L. F. 1971. Profile of mood states (POMS). San Diego: Educational and Industrial Testing Service.
43. 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.
44. 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.
45. Pak, A., and Paroubek, P. 2010 (May). Twitter as a corpus for sentiment analysis and opinion mining. In N. C. C. Chair, K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner, and D. Tapias (eds.), Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC'10), Valletta, Malta; ELRA, pp. 19-21. European Language Resources Association.
46. Pang, B., and Lee, L. 2008 (January). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2): 1-135.
47. 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.
48. 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.
49. Petrovíc, 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.
50. Prabowo, R., and Thelwall, M. 2009. Sentiment analysis: a combined approach. Journal of Informetrics 3(2): 143-57.
51. 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.
52. 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.
53. 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.
54. 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.
55. 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.
56. 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.
57. 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.
58. 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.
59. Tsytsarau, M. and Palpanas, T. 2012. Survey on mining subjective data on the web. Data Mining and Knowledge Discovery 24: 478-514.
60. 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.
61. 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, Jurgens, and Schoen. Social Science Computer Review 30(2): 235-9.
62. Tunkelang, D. 2009. A Twitter analog to pagerank. http://thenoisychannel. com/2009/01/13/a-twitter-analog-to-pagerank/
63. 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.
64. 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.
65. 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.