Topic-based sentiment analysis for the social web: The role of mood and issue-related words

Journal of the American Society for Information Science and Technology

Volumn 64, Issue 8, 2013, Pages 1608-1617

  Thelwall, Mike ^a   Buckley, Kevan ^a  

^a UNIVERSITY OF WOLVERHAMPTON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ACADEMIC RESEARCH; DATA JOURNALISMS; OFFLINE; ON-LINE COMMUNICATION; SENTIMENT ANALYSIS; SENTIMENT LEXICONS; SHEDDING LIGHT; SOCIAL WEBS;

ARTIFICIAL INTELLIGENCE; SOFTWARE ENGINEERING;

DATA MINING;

EID: 84880302858     PISSN: 15322882     EISSN: 15322890     Source Type: Journal    
DOI: 10.1002/asi.22872     Document Type: Article

References (36)

1. Agerri, R., &, García-Serrano, A., (2010). Q-WordNet: Extracting polarity from WordNet senses. Proceedings of the Seventh conference on International Language Resources and Evaluation, Retrieved May 25, 2010, from: http://www.lrec-conf.org/proceedings/lrec2010/pdf/2695-Paper.pdf.
2. Aue, A., &, Gamon, M., (2005). Customizing sentiment classifiers to new domains: A case study. Proceedings of the International Conference on Recent Advances in Natural Language Processing, Retrieved March 6, 2011, from: http://research.microsoft.com/pubs/65430/new-domain-sentiment.pdf.
3. Baccianella, S., Esuli, A., &, Sebastiani, F., (2010). SentiWordNet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. Proceedings of the Seventh conference on International Language Resources and Evaluation, Retrieved May 25, 2010, from: http://www.lrec-conf.org/proceedings/ lrec2010/pdf/2769-Paper.pdf.
4. Blitzer, J., McDonald, R., &, Pereira, F., (2006). Domain adaptation with structural correspondence learning. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing (EMNLP 2006; pp. 120-128).
5. Chung, J. E., &, Mustafaraj, E., (2011). Can collective sentiment expressed on Twitter predict political elections? In, W. Burgard, &, D. Roth, (Eds.), Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2011) (pp. 1768-1769). Menlo Park, CA: AAAI Press.
6. Ding, X., Liu, B., &, Yu, P.S., (2008). A holistic lexicon-based approach to opinion mining. In: Proceedings of the Conference on Web Search and Web Data Mining (WSDM-2008) (pp. 231-240). New York: ACM Press.
7. Dodds, P.S., &, Danforth, C.M., (2010). Measuring the happiness of large-scale written expression: Songs, blogs, and presidents. Journal of Happiness Studies, 11 (4), 441-456.
8. Glorot, X., Bordes, A., &, Bengio, Y., (2011). Domain adaptation for large-scale sentiment classification: A deep learning approach. In Proceedings of the 28 th International Conference on Machine Learning (ICML 2011).
9. Gruzd, A., Doiron, S., &, Mai, P., (2011). Is happiness contagious online? A case of Twitter and the 2010 Winter Olympics. In Proceedings of the 44th Hawaii International Conference on System Sciences. Retrieved June 2, 2011, from: http://ieeexplore.ieee.org/xpl/freeabs-all.jsp?tp=&arnumber=5718715. Washington, DC: IEEE Computer Society.
10. Guardian, (2012). Data journalism and data visualisation. http://www.guardian.co.uk/data.
11. Hatzivassiloglou, V., &, McKeown, K.R., (1997). Predicting the semantic orientation of adjectives. In:, P. Cohen, &, W. Wahlster, (Eds). Proceedings of Annual Meeting of the Association for Computational Linguistics (ACL-1997) (pp. 174-181). Madrid, Spain 7-12 July 1997.
12. Hu, M., &, Liu, B., (2004). Mining and summarizing customer reviews. Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '04) (pp. 168-177). New York: ACM Press.
13. Kaji, N., &, Kitsuregawa, M., (2007). Building lexicon for sentiment analysis from massive collection of HTML documents. In: Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2007) (pp. 1075-1083). June 28-30, Prague, Czech Republic.
14. Kramer, A.D.I., (2010). An unobtrusive behavioral model of "gross national happiness." In Proceedings of CHI 2010 (pp. 287-290). New York: ACM Press.
15. Liu, B., (2012). Sentiment analysis and opinion mining. San Rafael, CA: Morgan and Claypool.
16. Lu, Y., Kong, X., Quan, X., Liu, W., &, Xu, Y., (2010). Exploring the sentiment strength of user reviews. Lecture Notes in Computer Science, 6184, 471-482.
17. Lu, Y., Castellanos, M., Dayal, U., &, Zhai, C., (2011). Automatic construction of a context-aware sentiment lexicon: An optimization approach. In Proceedings of the 20th international conference on World Wide Web (WWW '2011) (pp. 347-356). New York: ACM Press.
18. Mejova, Y., &, Srinivasan, P., (2011). Exploring feature definition and selection for sentiment classifiers. In: Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media (ICWSM-2011) (pp. 546-549). Menlo Park, CA: AIII Press.
19. Pang, B., &, Lee, L., (2008). Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 1 (1-2), 1-135.
20. Pang, B., Lee, L., &, Vaithyanathan, S., (2002). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp. 79-86). Morristown, NJ: Association for Computational Linguistics.
21. Pennebaker, J., Mehl, M., &, Niederhoffer, K., (2003). Psychological aspects of natural language use: Our words, our selves. Annual Review of Psychology, 54, 547-577.
22. Ponomareva, N., &, Thelwall, M., (2012). Do neighbours help? An exploration of graph-based algorithms for cross-domain sentiment classification. The 2012 Conference on Empirical Methods on Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2012; pp. 655-665).
23. Riloff, E., Patwardhan, S., &, Wiebe, J., (2006). Feature subsumption for opinion analysis. Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 440-448.
24. Stone, P.J., Dunphy, D.C., Smith, M.S., &, Ogilvie, D.M., (1966). The General Inquirer: A Computer Approach to Content Analysis. Cambridge, MA: The MIT Press.
25. Strapparava, C., &, Mihalcea, R., (2008). Learning to identify emotions in text, Proceedings of the 2008 ACM Symposium on Applied Computing (pp. 1556-1560). New York, NY: ACM.
26. Strapparava, C., Valitutti, A., &, Stock, O., (2006). The affective weight of lexicon. Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC 2006), Retrieved July 28, 2011, from: http://gandalf.aksis.uib.no/lrec2006/pdf/2186-pdf.pdf.
27. Taboada, M., Anthony, C., &, Voll, K., (2006). Methods for creating semantic orientation dictionaries. In Proceedings of Fifth International Conference on Language Resources and Evaluation (LREC 2006) (pp. 427-432). Genoa, Italy: ELRA.
28. Taboada, M., Brooke, J., Tofiloski, M., Voll, K., &, Stede, M., (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37 (2), 267-307.
29. Tan, S., Wu, G., Tang, H., &, Cheng, X., (2007). A novel scheme for domain-transfer problem in the context of sentiment analysis. In Proceedings of the sixteenth ACM conference on Conference on information and knowledge management (CIKM 2007) (pp. 979-982). New York, NY: ACM Press.
30. Thelwall, M., Buckley, K., Paltoglou, G., Cai, D., &, Kappas, A., (2010). Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology, 61 (12), 2544-2558.
31. Thelwall, M., Buckley, K., &, Paltoglou, G., (2012). Sentiment strength detection for the social web. Journal of the American Society for Information Science and Technology, 63 (1), 163-173.
32. Turney, P.D., (2002). 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) (pp. 417-424). July 6-12, 2002, Philadelphia, PA.
33. Wilson, T., (2008). Fine-Grained Subjectivity and Sentiment Analysis: Recognizing the Intensity, Polarity, and Attitudes of Private States. Pittsburgh: University of Pittsburgh.
34. Wu, Q., Tan, S., &, Cheng, X., (2009). Graph ranking for sentiment transfer. In Proceedings of the ACL-IJCNLP 2009 Conference (ACL-IJCNLP '09; pp. 317-320).
35. Zagibalov, T., (2010). Unsupervised and Knowledge-Poor Approaches to Sentiment Analysis. Brighton, Un ited Kingdom: University of Sussex.
36. Zhang, L., &, Liu, B., (2011). Identifying noun product features that imply opinions. In Proceedings of the Annual Meeting of the Association for Computational Linguistics-Human Language Technologies (HLT '11) (pp. 575-580). Stroudsburg, PA: ACL Press.