Content vs. context for sentiment analysis: A comparative analysis over microblogs

HT'12 - Proceedings of 23rd ACM Conference on Hypertext and Social Media

Volumn , Issue , 2012, Pages 187-196

  Aisopos, Fotis ^b   Papadakis, George ^a,b   Tserpes, Konstantinos ^b   Varvarigou, Theodora ^b  

^a L3S RESEARCH CENTER   (Germany)

^b NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

N gram graphs; Sentiment analysis; Social context

Indexed keywords

CLASSIFICATION METHODS; COMPARATIVE ANALYSIS; CONTENT-BASED FEATURES; CONTEXT-BASED; DIMENSIONALITY REDUCTION; DISCRETIZATIONS; EXTRACTION COSTS; INHERENT CHARACTERISTICS; MICRO-BLOG; MULTIPLE CLASSIFICATION; N-GRAM GRAPHS; NOISE-TOLERANT; REAL WORLD DATA; SENTIMENT ANALYSIS; SOCIAL CONTEXT; TIME EFFICIENCIES; TRADITIONAL TECHNIQUES;

HYPERTEXT SYSTEMS; VIRTUAL REALITY;

DATA MINING;

EID: 84864064486     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2309996.2310028     Document Type: Conference Paper

References (35)

1. L. Barbosa and J. Feng. Robust sentiment detection on twitter from biased and noisy data. In COLING, pages 36-44, 2010.
2. J. Bollen, H. Mao, and X. Zeng. Twitter mood predicts the stock market. Journal of Computational Science, 2011.
3. J. Bollen, A. Pepe, and H. Mao. Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. In ICWSM, 2011.
4. N. Christakis and J. Fowler. Connected: The surprising power of our social networks and how they shape our lives. Little, Brown and Company, 2009.
5. D. Davidov, O. Tsur, and A. Rappoport. Enhanced sentiment learning using twitter hashtags and smileys. In COLING, 2010.
6. J. Eisenstein, B. O'Connor, N. A. Smith, and E. P. Xing. A latent variable model for geographic lexical variation. In EMNLP, pages 1277-1287, 2010.
7. H. Escalante, T. Solorio, and M. Montesy Gómez. Local histograms of character n-grams for authorship attribution. In ACL, pages 288-298, 2011.
8. R. Fan, K. Chang, C. Hsieh, X. Wang, and C. Lin. Liblinear: A library for large linear classification. JMLR, 9:1871-1874, 2008.
9. G. Giannakopoulos, V. Karkaletsis, G. A. Vouros, and P. Stamatopoulos. Summarization system evaluation revisited: N-gram graphs. TSLP, 5(3), 2008.
10. G. Giannakopoulos and T. Palpanas. Content and type as orthogonal modeling features. International Journal of Advances on Networks and Services, 3(2), 2010.
11. A. Go, R. Bhayani, and L. Huang. Twitter sentiment classification using distant supervision. Processing, pages 1-6, 2009.
12. M. Hall. Correlation-based feature selection for machine learning. PhD thesis, University of Waikato, 1999.
13. M. Hurst, M. Siegler, and N. Glance. On estimating the geographic distribution of social media. In ICWSM, 2007.
14. L. Jiang, M. Yu, M. Zhou, X. Liu, and T. Zhao. Target-dependent Twitter sentiment classification. In COLING, 2011.
15. I. Kanaris, K. Kanaris, I. Houvardas, and E. Stamatatos. Words versus character n-grams for anti-spam filtering. IJAIT, 16(6):1047, 2007.
16. L. Kurgan and K. Cios. Caim discretization algorithm. IEEE TKDE, pages 145-153, 2004.
17. H. Kwak, C. Lee, H. Park, and S. Moon. What is Twitter, a social network or a news media? In WWW, 2010.
18. B. Liu. Web data mining. Springer, 2007.
19. C. Manning, P. Raghavan, and H. Schütze. Introduction to information retrieval. Cambridge University Press, 2008.
20. T. Nasukawa and J. Yi. Sentiment analysis: capturing favorability using natural language processing. In K-CAP, 2003.
21. B. O'Connor, R. Balasubramanyan, B. R. Routledge, and N. A. Smith. From tweets to polls: Linking text sentiment to public opinion time series. In ICWSM, 2010.
22. A. Pak and P. Paroubek. Twitter as a corpus for sentiment analysis and opinion mining. In LREC, 2010.
23. B. Pang and L. Lee. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2008.
24. G. Shao. Understanding the appeal of user-generated media: a uses and gratification perspective. Internet Research, 2009.
25. G. Somprasertsri, P. Lalitrojwong, and P. Lalitrojwong. Mining feature-opinion in online customer reviews for opinion summarization. Journal of Univ. Comp. Science, 2010.
26. M. Speriosu, N. Sudan, S. Upadhyay, and J. Baldridge. Twitter polarity classification with label propagation over lexical links and the follower graph. In EMNLP, pages 53-63, 2011.
27. C. Tan, L. Lee, J. Tang, L. Jiang, M. Zhou, and P. Li. User-level sentiment analysis incorporating social networks. In KDD, 2011.
28. H. Tang, S. Tan, and X. Cheng. A survey on sentiment detection of reviews. Expert Systems with Applications, 2009.
29. K. Tsagkalidou, V. Koutsonikola, A. Vakali, and K. Kafetsios. Emotional aware clustering on micro-blogging sources. In ACII, 2011.
30. M. Tsytsarau and T. Palpanas. Survey on mining subjective data on the web. Data Mining and Knowledge Discovery Journal, 2011.
31. A. Tumasjan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe. Predicting elections with twitter: What 140 characters reveal about political sentiment. In ICWSM, 2010.
32. J. Weng, E.-P. Lim, J. Jiang, and Q. He. Twitterrank: finding topic-sensitive influential twitterers. In WSDM, 2010.
33. T. Wilson and S. Raaijmakers. Comparing word, character, and phoneme n-grams for subjective utterance recognition. In INTERSPEECH, 2008.
34. I. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2005.
35. J. Yang and J. Leskovec. Patterns of temporal variation in online media. In WSDM, pages 177-186, 2011.