Sentiment Analysis in Social Streams

  • Hassan Saif
  • F. Javier Ortega
  • Miriam Fernández
  • Iván Cantador
Chapter

Abstract

In this chapter, we review and discuss the state of the art on sentiment analysis in social streams—such as web forums, microblogging systems, and social networks, aiming to clarify how user opinions, affective states, and intended emotional effects are extracted from user generated content, how they are modeled, and how they could be finally exploited. We explain why sentiment analysis tasks are more difficult for social streams than for other textual sources, and entail going beyond classic text-based opinion mining techniques. We show, for example, that social streams may use vocabularies and expressions that exist outside the mainstream of standard, formal languages, and may reflect complex dynamics in the opinions and sentiments expressed by individuals and communities.

References

  1. 1.
    Agrawal, S., Siddiqui, T.J.: Using syntactic and contextual information for sentiment polarity analysis. In: Proceedings of the 2nd International Conference on Interaction Sciences Information Technology, Culture and Human (ICIS’09), pp. 620–623 (2009)Google Scholar
  2. 2.
    Amichai-Hamburger, Y., Vinitzkyb, G.: Social network use and personality. Comput. Hum. Behav. 26(6), 1289–1295Google Scholar
  3. 3.
    Aue, A., Gamon, M.: Customizing sentiment classifiers to new domains: a case study. In: Proceedings of the 3rd International Conference on Recent Advances in Natural Language Processing (RANLP’05) (2005)Google Scholar
  4. 4.
    Baccianella, S., Esuli, A., Sebastiani, F.: entiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC’10) (2010)Google Scholar
  5. 5.
    Bentivogli, L., Forner, P., Magnini, B., Pianta, E.: Revising WordNet domains hierarchy: semantics, coverage, and balancing. In: Proceedings of the COLLING’04 Workshop on Multilingual Linguistic Resources (MLR’04), pp. 101–108 (2004)Google Scholar
  6. 6.
    Bhuiyan, S.I.: Social media and its effectiveness in the political reform movement in Egypt. Middle East Media Educ. 1(1), 14–20 (2011)Google Scholar
  7. 7.
    Bifet, A., Frank, E.: Sentiment knowledge discovery in twitter streaming data. In: Proceedings of the 13th International Conference on Discovery Science (DS’10), pp. 1–15 (2010)Google Scholar
  8. 8.
    Cambria, E., Schuller, B., Xia, Y., Havasi, C.: New avenues in opinion mining and sentiment analysis. IEEE Intell. Syst. 28(2), 15–21 (2013)CrossRefGoogle Scholar
  9. 9.
    Cambria, E., Song, Y., Wang, H., Howard, N.: Semantic multi-dimensional scaling for open-domain sentiment analysis. IEEE Intell. Syst. 29(2), 44–51 (2013)CrossRefGoogle Scholar
  10. 10.
    Cantador, I., Konstas, I., Jose, J.M.: Categorising social tags to improve Folksonomy-based recommendations. J. Web Semant. 9(1), 1–15 (2010)CrossRefGoogle Scholar
  11. 11.
    Carter, S., Weerkamp, W., Tsagkias, M.: Microblog language identification: overcoming the limitations of short, unedited and idiomatic text. Lang. Resour. Eval. 47(1), 195–215 (2013)CrossRefGoogle Scholar
  12. 12.
    Carvalho, P., Sarmento, L., Silva, M.J., de Oliveira, E.: Clues for detecting irony in user-generated contents: Oh...!! It’s "so easy" ;-) In: Proceedings of the 1st International Workshop on Topic-sentiment Analysis for Mass Opinion (TSA’09), pp. 53–56 (2009)Google Scholar
  13. 13.
    Chow, A., Foo, M.-H. N., Manai, G.: HybridRank: a hybrid content-based approach to mobile game recommendations. In: Proceedings of the 1st Workshop on New Trends in Content-based Recommender Systems (CBRecSys’14), pp. 1–4 (2014)Google Scholar
  14. 14.
    Corr, P.J.: The reinforcement sensitivity theory. In: Corr, P.J. (ed.) The Reinforcement Senitivity Theory of Personality. Cambridge University Press (2008)Google Scholar
  15. 15.
    Cruz, F.L., Vallejo, C.G., Enríquez, F., Troyano, J.A.: PolarityRank: finding an equilibrium between followers and contraries in a network. Inf. Process. Manage. 48(2), 271–282 (2012)CrossRefGoogle Scholar
  16. 16.
    Cruz, F.L., Troyano, J.A., Enríquez, F., Ortega, F.J., Vallejo, C.G.: Long autonomy or long delay? The importance of domain in opinion mining. Expert Syst. Appl. 40(8), 3174–3184 (2013)CrossRefGoogle Scholar
  17. 17.
    Cruz, F.L., Troyano, J.A., Pontes, B., Ortega, F.J.: Building layered, multilingual sentiment lexicons at synset and lemma levels. Expert Syst. Appl. 41(13), 5984–5994 (2014)CrossRefGoogle Scholar
  18. 18.
    Davidov, D., Tsur, O., Rappoport, A.: Semi-supervised recognition of sarcastic sentences in Twitter and Amazon. In: Proceedings of the 14th Conference on Computational Natural Language Learning (CoNLL’10), pp. 107–116 (2010)Google Scholar
  19. 19.
    Dehkharghani, R., Yanikoglu, B., Tapucu, D., Saygin, Y.: Adaptation and use of subjectivity lexicons for domain dependent sentiment classification. In: Proceedings of the 12th IEEE International Conference on Data Mining Workshops, pp. 669–673 (2012)Google Scholar
  20. 20.
    Barbagallo, D., Bruni, L., Francalanci, C., Giacomazzi, P.: An empirical study on the relationship between twitter sentiment and influence in the tourism domain. In: Information and Communication Technology in Tourism, pp. 506–516 (2012)Google Scholar
  21. 21.
    Durant, K.T., Smith, M.D.: Mining sentiment classification from political web logs. In: Proceedings of the WebKDD’06 Workshop on Web Mining and Web Usage Analysis (2006)Google Scholar
  22. 22.
    Elahi, M.F., Monachesi, P.: An examination of cross-cultural similarities and differences from social media data with respect to language use. In: Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC’12), pp. 4080–4086 (2012)Google Scholar
  23. 23.
    Feng, Y., Zhuang, Y., Pan, Y.: Music information retrieval by detecting mood via computational media aesthetics. In: Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence (WI’03), pp. 235–241 (2003)Google Scholar
  24. 24.
    Fernández, M., Allen, B., Wandhoefer, T., Cano, E., Alani, H.: Using social media to inform policy making: to whom are we listening? In: Proceedings of the 1st European Conference on Social Media (ECSM’14), pp. 174–182 (2014)Google Scholar
  25. 25.
    Fernández-Tobías, I., Cantador, I.: Personality-aware collaborative filtering: an empirical study in multiple domains with facebook data. In: Proceedings of the 15th International Conference on E-Commerce and Web Technologies (EC-Web’14), pp. 125–137 (2013)Google Scholar
  26. 26.
    Fernández-Tobías, I., Cantador, I., Plaza, L.: An emotion dimensional model based on social tags: crossing folksonomies and enhancing recommendations. In: Proceedings of the 14th International Conference on E-Commerce and Web Technologies (EC-Web’13), pp. 88–100 (2013)Google Scholar
  27. 27.
    Gangemi, A., Presutti, V., Reforgiato Recupero, D.: Frame-based detection of opinion holders and topics: a model and a tool. IEEE Comput. Intell. Mag. 9(1), 20–30 (2014)CrossRefGoogle Scholar
  28. 28.
    Hancock, J.T., Cardie, C.: Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics (HLT’11), pp. 309–319 (2011)Google Scholar
  29. 29.
    Hatzivassiloglou, V., McKeown, K.R.: Predicting the semantic orientation of adjectives. In: Proceedings of the 35th Annual Meeting on Association for Computational Linguistics (ACL’98), pp. 174–181 (1998)Google Scholar
  30. 30.
    Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proceedings of the 2004 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’04), pp. 168–177 (2004)Google Scholar
  31. 31.
    Hu, M., Liu, B.: Opinion feature extraction using class sequential rules. In: Proceedings of the AAAI’06 Spring Symposium: Computational Approaches (2006)Google Scholar
  32. 32.
    Jia, L., Yu, C., Meng, W.: The effect of negation on sentiment analysis and retrieval effectiveness. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM’10), pp. 1827–1830 (2010)Google Scholar
  33. 33.
    Jindal, N., Liu, B.: Opinion spam and analysis. In: Proceedings of the 2008 International Conference on Web Search and Data Mining (WSDM’08), pp. 219–230 (2008)Google Scholar
  34. 34.
    Kamps, J., Marx, M., Mokken, R.J., Rijke, M.: Using WordNet to measure semantic orientations of adjectives. In: Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC’04), pp. 1115–1118 (2004)Google Scholar
  35. 35.
    Kaplan, A.M., Haenlein, M.: Users of the World, unite! the challenges and opportunities of social media. Bus. Horiz. 53(1), 59–68 (2010)CrossRefGoogle Scholar
  36. 36.
    Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, 2nd edn. Springer (2011)Google Scholar
  37. 37.
    Long, J., Yu, M., Zhou, M., Liu, X., Zhao, T.: Target-dependent twitter sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (HLT’11), pp. 151–160 (2011)Google Scholar
  38. 38.
    Maynard, D., Bontcheva, K., Rout, D.: Challenges in developing opinion mining tools for social media. In: Proceedings of NLP can u tag# usergeneratedcontent?! Workshop (2012)Google Scholar
  39. 39.
    Maynard, D., Gossen, G., Funk, A., Fisichella, M.: Should I care about your opinion? Detection of opinion interestingness and dynamics in social media. Future Internet 6(3), 457–481 (2014)CrossRefGoogle Scholar
  40. 40.
    Meng, X., Wei, F., Liu, X., Zhou, M., Li, S., Wang, H.: Entity-centric topic-oriented opinion summarization in twitter. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’12), pp. 379–387 (2012)Google Scholar
  41. 41.
    Mihalcea, R., Strapparava, C.: Making computers laugh. In: Proceedings of the 2005 Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT’05), pp. 531–538 (2005)Google Scholar
  42. 42.
    Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  43. 43.
    Miller, L., Williamson, A.: Digital Dialogs—Third Phase Report. Handsard Society (2008)Google Scholar
  44. 44.
    Morinaga, S., Yamanishi, K., Tateishi, K., Fukushima, T.: Mining product reputations on the web. In: Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’02), pp. 341–349 (2002)Google Scholar
  45. 45.
    O’Connor, B., Balasubramanyan, R., Routledge, B.R., Smith, N.A.: From tweets to polls: linking text sentiment to public opinion time series. In: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media (ICWSM’10), pp. 122–129 (2010)Google Scholar
  46. 46.
    Ortega, F.J.: Detection of dishonest behaviors in on-line networks using graph-based ranking techniques. AI Commun. 26(3), 327–329 (2013)Google Scholar
  47. 47.
    Ott, M., Cardie, C., Hancock, J.T.: Negative deceptive opinion spam. In: Proceedings of 2013 Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT’13), pp. 497–501 (2013)Google Scholar
  48. 48.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)CrossRefGoogle Scholar
  49. 49.
    Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of the 5th International Conference on Language Resources and Evaluation (LREC’10), pp. 1320–1326 (2010)Google Scholar
  50. 50.
    Peddinti, V.M.K., Chintalapoodi, P.: Domain adaptation in sentiment analysis of twitter. In: Analyzing Microtext, vol. WS-11-05 of AAAI’11 Workshops (2011)Google Scholar
  51. 51.
    Pérez-Rosas, V., Banea, C., Mihalcea, R.: Learning sentiment lexicons in spanish. In: Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC’12), pp. 3077–3081 (2012)Google Scholar
  52. 52.
    Popescu, A.-M., Etzioni, O.: Extracting product features and opinions from reviews. In: Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT/EMNLP’05), pp. 339–346 (2005)Google Scholar
  53. 53.
    Qiu, G., Liu, B., Bu, J., Chen, C.: Opinion word expansion and target extraction through double propagation. Comput. Linguist. 37(1), 9–27 (2011)CrossRefGoogle Scholar
  54. 54.
    Recupero, D.R., Presutti, V., Consoli, S., Gangemi, A., Nuzzolese, A.G.: Sentilo: frame-based sentiment analysis. Cogn. Comput. 1–15 (2014)Google Scholar
  55. 55.
    Revelle, W.: Personality processes. Annu. Rev. Psychol. 46, 295–328 (1995)CrossRefGoogle Scholar
  56. 56.
    Reyes, A., Rosso, P., Buscaldi, D.: From humor recognition to irony detection: the figurative language of social media. Data Knowl. Eng. 74, 1–12 (2012)CrossRefGoogle Scholar
  57. 57.
    Riloff, E., Wiebe, J., Wilson, T.: Learning subjective nouns using extraction pattern bootstrapping. In: Proceedings of the 7th Conference on Computational Natural Language Learning (CoNLL’03), vol. 4, pp. 25–32 (2003)Google Scholar
  58. 58.
    Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)CrossRefGoogle Scholar
  59. 59.
    Saif, H., He, Y., Alani, H.: Semantic sentiment analysis of twitter. In: Proceedings of the 11th International Semantic Web Conference (ISWC’12), pp. 508–524 (2012)Google Scholar
  60. 60.
    Saif, H., He, Y., Alani, H.: Alleviating data sparsity for twitter sentiment analysis. In: Proceedings of the WWW’12 Workshop on Making Sense of Microposts (2012)Google Scholar
  61. 61.
    Saif, H., Fernández, M., He, Y., Alani, H.: SentiCircles for contextual and conceptual semantic sentiment analysis of twitter. In: Proceedings of the 11th Extended Semantic Web Conference (ESWC’14), pp. 83–98 (2014)Google Scholar
  62. 62.
    Saif, H., Fernández, M., He, Y., Alani, H.: On stopwords, filtering and data sparsity for sentiment analysis of twitter. In: Proceedings of the 9th International Conference on Language Resources and Evaluation (LREC’14), pp. 810–817 (2014)Google Scholar
  63. 63.
    Saif, H., He, Y., Fernández, M., Alani, H.: Semantic patterns for sentiment analysis of twitter. In: Proceedings of the 13th International Semantic Web Conference (ISWC’14)—part 2, pp. 324–340 (2014)Google Scholar
  64. 64.
    Sebastiani, F., Esuli, A.: Determining term subjectivity and term orientation for opinion mining. In: Proceedings of the 11th Conference of the European Chapter of the Association for Computational Linguistics (EACL’06) (2006)Google Scholar
  65. 65.
    Silva, I.S., Gomide, J., Veloso, A., Meira Jr, W., Ferreira, R.: Effective sentiment stream analysis with self-augmenting training and demand-driven projection. In: Proceedings of the 34th international ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’11), pp. 475–484 (2011)Google Scholar
  66. 66.
    Simon, T., Goldberg, A., Aharonson-Daniel, L., Leykin, D., Adini, B.: Twitter in the cross fire—the use of social media in the Westgate Mall terror attack in Kenya. PloS one 9(8), e104136 (2014)CrossRefGoogle Scholar
  67. 67.
    Strapparava, C., Mihalcea, R.: Learning to identify emotions in text. In: Proceedings of the 2008 ACM Symposium on Applied Computing (SAC’08), pp. 1556–1560 (2008)Google Scholar
  68. 68.
    Strapparava, C., Valitutti, A.: Wordnet-affect: an affective extension of WordNet. In: Proceedings of the 4th International Conference on Language Resources and Evaluation (LREC’04), pp. 1083–1086 (2004)Google Scholar
  69. 69.
    Stone, P.J., Dunphy, D.C., Smith, M.S.: The General Inquirer: A Computer Approach to Content Analysis. MIT Press (1966)Google Scholar
  70. 70.
    Szomszor, M., Cantador, I., Alani, H.: Correlating user pprofiles from multiple folksonomies. In: Proceedings of the 19th ACM Conference on Hypertext and Hypermedia (Hypertext’08), pp. 33–42 (2008)Google Scholar
  71. 71.
    Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)CrossRefGoogle Scholar
  72. 72.
    Tkalcic, M., Burnik, U., Odic, A., Kosir, A., Tasic, J.: Emotion-aware recommender systems—a framework and a case study. In: Markovski, S., Gusev, M. (eds.) ICT Innovations 2012. Advances in Intelligent Systems and Computing 207, pp. 141–150. Springer (2013)Google Scholar
  73. 73.
    Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., Kappas, A.: Sentiment strength detection in short informal text. J. Am. Soc. Inf. Sci. Technol. 61(12), 2544–2558 (2010)CrossRefGoogle Scholar
  74. 74.
    Thelwall, M., Wilkinson, D., Uppal, S.: Data mining emotion in social network communication: gender differences in MySpace. J. Am. Soc. Inf. Sci. Technol. 61(1), 190–199 (2010)CrossRefGoogle Scholar
  75. 75.
    Thelwall, M., Buckley, K., Paltoglou, G.: Sentiment strength detection for the social web. J. Am. Soc. Inf. Sci. Technol. 63(1), 163–173 (2012)CrossRefGoogle Scholar
  76. 76.
    Thomas, K., Fernández, M., Brown, S., Alani, H.: OUSocial2—a platform for gathering students’ feedback from social media. In: Demo at the 13th International Semantic Web Conference (ISWC’14) (2014)Google Scholar
  77. 77.
    Tumasjan, A., Sprenger, T.O., Sandner, P.G., Welpe, I.M.: Predicting elections with twitter: what 140 characters reveal about political sentiment. In: Proceedings of the 4th International AAAI Conference on Weblogs and Social Media (ICWSM’10), pp. 178–185 (2010)Google Scholar
  78. 78.
    Turney, P.: Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL’02), pp. 417–424 (2002)Google Scholar
  79. 79.
    Volkova, S., Wilson, T., Yarowsky, D.: Exploring demographic language variations to improve multilingual sentiment analysis in social media. In: EMNLP, pp. 1815–1827 (2013)Google Scholar
  80. 80.
    Vorderer, P., Klimmt, C., Ritterfeld, U.: At the heart of media entertainment. Commun. Theory 14(4), 388–408 (2004)CrossRefGoogle Scholar
  81. 81.
    Wiebe, J., Bruce, R., O’Hara, T.: Development and use of a gold standard data set for subjectivity classifications. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (ACL’99), pp. 246–253 (1999)Google Scholar
  82. 82.
    Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Lang. Resour. Eval. 39(2–3), 165–210 (2006)Google Scholar
  83. 83.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity in phrase-level sentiment analysis. In: Proceedings of the 2005 Conference on Human Language Technology and Empirical Methods in Natural Language Processing (HLT’05), pp. 347–354 (2005)Google Scholar
  84. 84.
    Wilson, T., Wiebe, J., Hoffmann, P.: Recognizing contextual polarity: an exploration of features for phrase-level sentiment analysis. Comput. Linguist. 35(3), 399–433 (2009)CrossRefGoogle Scholar
  85. 85.
    Yerva, S.R., Mikls, Z., Aberer, K.: Entity-based classification of twitter messages. Int. J. Comput. Sci. Appl. 9, 88–115 (2012)Google Scholar
  86. 86.
    Yu, H., Hatzivassiloglou, V.: Towards answering opinion questions: Separating facts from opinions and identifying the polarity of opinion sentences. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing (EMNLP’03), pp. 129–136 (2003)Google Scholar
  87. 87.
    Zhang, J., Tang, J., Li, J.: Expert finding in a social network. In: Proceedings of the 12th International Conference on Database Systems for Advanced Applications (DASFAA’07), pp. 1066–1069 (2007)Google Scholar
  88. 88.
    Zillmann, D.: Mood management: using entertainment to full advantage. In: Donohew, L., Sypher, H.E., Higgins, E.T. (eds.) Communication, Social Cognition, and Affect, pp. 147–171. Lawrence Erlbaum Associates (1988)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Hassan Saif
    • 1
  • F. Javier Ortega
    • 2
  • Miriam Fernández
    • 3
  • Iván Cantador
    • 4
  1. 1.Knowledge Media InstituteMilton KeynesUK
  2. 2.Universidad de SevillaSevilleSpain
  3. 3.Knowledge Media InstituteMilton KeynesUK
  4. 4.Universidad Autónoma de MadridMadridSpain

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