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Erschienen in:
Introduction to Emotions and Personality in Personalized Systems Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

7. Sentiment Analysis in Social Streams

verfasst von : Hassan Saif, F. Javier Ortega, Miriam Fernández, Iván Cantador

Erschienen in: Emotions and Personality in Personalized Services

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Computing Technologies for Social Signals
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Literatur
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)
2.
Amichai-Hamburger, Y., Vinitzkyb, G.: Social network use and personality. Comput. Hum. Behav. 26(6), 1289–1295
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)
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)
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)
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)
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)
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)CrossRef
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)CrossRef
10.
Cantador, I., Konstas, I., Jose, J.M.: Categorising social tags to improve Folksonomy-based recommendations. J. Web Semant. 9(1), 1–15 (2010)CrossRef
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)CrossRef
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)
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)
14.
Corr, P.J.: The reinforcement sensitivity theory. In: Corr, P.J. (ed.) The Reinforcement Senitivity Theory of Personality. Cambridge University Press (2008)
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)CrossRef
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)CrossRef
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)CrossRef
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)CrossRef
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)
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)
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)
31.
Hu, M., Liu, B.: Opinion feature extraction using class sequential rules. In: Proceedings of the AAAI’06 Spring Symposium: Computational Approaches (2006)
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)
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)
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)
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)CrossRef
36.
Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data, 2nd edn. Springer (2011)
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)
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)
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)CrossRef
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)
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)
42.
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)CrossRef
43.
Miller, L., Williamson, A.: Digital Dialogs—Third Phase Report. Handsard Society (2008)
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)
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)
46.
Ortega, F.J.: Detection of dishonest behaviors in on-line networks using graph-based ranking techniques. AI Commun. 26(3), 327–329 (2013)
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)
48.
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retr. 2(1–2), 1–135 (2008)CrossRef
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)
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)
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)
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)
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)CrossRef
54.
Recupero, D.R., Presutti, V., Consoli, S., Gangemi, A., Nuzzolese, A.G.: Sentilo: frame-based sentiment analysis. Cogn. Comput. 1–15 (2014)
55.
Revelle, W.: Personality processes. Annu. Rev. Psychol. 46, 295–328 (1995)CrossRef
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)CrossRef
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)
58.
Russell, J.A.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)CrossRef
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)
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)
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)
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)
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)
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)
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)
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)CrossRef
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)
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)
69.
Stone, P.J., Dunphy, D.C., Smith, M.S.: The General Inquirer: A Computer Approach to Content Analysis. MIT Press (1966)
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)
71.
Taboada, M., Brooke, J., Tofiloski, M., Voll, K., Stede, M.: Lexicon-based methods for sentiment analysis. Comput. Linguist. 37(2), 267–307 (2011)CrossRef
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)
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)CrossRef
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)CrossRef
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)CrossRef
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)
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)
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)
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)
80.
Vorderer, P., Klimmt, C., Ritterfeld, U.: At the heart of media entertainment. Commun. Theory 14(4), 388–408 (2004)CrossRef
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)
82.
Wiebe, J., Wilson, T., Cardie, C.: Annotating expressions of opinions and emotions in language. Lang. Resour. Eval. 39(2–3), 165–210 (2006)
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)
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)CrossRef
85.
Yerva, S.R., Mikls, Z., Aberer, K.: Entity-based classification of twitter messages. Int. J. Comput. Sci. Appl. 9, 88–115 (2012)
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)
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)
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)
Metadaten
Titel
Sentiment Analysis in Social Streams
verfasst von
Hassan Saif
F. Javier Ortega
Miriam Fernández
Iván Cantador
Copyright-Jahr
2016
Buch
Emotions and Personality in Personalized Services

Print ISBN: 978-3-319-31411-2

Electronic ISBN: 978-3-319-31413-6

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-31413-6_7

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