Generate Adjective Sentiment Dictionary for Social Media Sentiment Analysis Using Constrained Nonnegative Matrix Factorization

Proceedings of the 5th International AAAI Conference on Weblogs and Social Media, ICWSM 2011

Volumn , Issue , 2011, Pages 273-280

  Peng, Wei ^a   Park, Dae Hoon ^b  

^a XEROX CORPORATION   (United States)

^b University of Illinois at Urbana Champaign   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; LEARNING SYSTEMS; MATRIX ALGEBRA; MATRIX FACTORIZATION; SOCIAL NETWORKING (ONLINE); SUPERVISED LEARNING;

AMAZON'S MECHANICAL TURKS; LEXICON-BASED; MACHINE LEARNING APPROACHES; MOVIE REVIEWS; NONNEGATIVE MATRIX FACTORIZATION; PRODUCT REVIEWS; SENTIMENT ANALYSIS; SENTIMENT DICTIONARIES; SOCIAL MEDIA; SOCIAL MEDIA DATUM;

SENTIMENT ANALYSIS;

EID: 85077250533     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. Dave, K.; Lawrence, S.; and Pennock, D. M. 2003. Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In WWW’03, 519–528.
2. Ding, C.; Li, T.; Peng, W.; and Park, H. 2006. Orthogonal nonnegative matrix t-factorizations for clustering. In KDD’06, 126–135.
3. Ding, C.; He, X.; and Simon, H. D. 2005. On the equivalence of nonnegative matrix factorization and spectral clustering. In Proceedings of the SIAM Data Mining Conference, 606–610.
4. Ding, C.; Li, T.; and Peng, W. 2008. On the equivalence between non-negative matrix factorization and probabilistic latent semantic indexing. Computation Statistics and Data Analysis 52(8):3913–3927.
5. Esuli, A., and Sebastiani, F. 2006. Sentiwordnet: A publicly available lexical resource for opinion mining. In LREC06, 417–422.
6. Fellbaum, C., ed. 1998. WordNet: An Electronic Lexical Database. Cambridge, MA: MIT Press.
7. Hatzivassiloglou, V., and McKeown, K. R. 1997. Predicting the semantic orientation of adjectives. In EACL’97, 174–181.
8. Hofmann, T. 2001. Unsupervised learning by probabilistic latent semantic analysis. Machine Learning 42(1-2):177–196.
9. Hu, M., and Liu, B. 2004. Mining and summarizing customer reviews. In KDD’04, 168–177.
10. Kamps, J.; Marx, M.; Mokken, R. J.; and Rijke, M. D. 2004. Using wordnet to measure semantic orientation of adjectives. In National Institute for, 1115–1118.
11. Kim, S.-M., and Hovy, E. 2004. Determining the sentiment of opinions. In COLING’04, 1367.
12. Lee, D. D., and Seung, S. H. 1999. Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788–791.
13. Li, T.; Ding, C.; and Jordan, M. I. 2007. Solving consensus and semi-supervised clustering problems using nonnegative matrix factorization. In ICDM’07, 577–582.
14. Pang, B.; Lee, L.; and Vaithyanathan, S. 2002. Thumbs up?: sentiment classification using machine learning techniques. In EMNLP’02, 79–86.
15. Turney, P. D. 2002. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In ACL’02, 417–424.
16. Wilson, T.; Wiebe, J.; and Hoffmann, P. 2005a. Recognizing contextual polarity in phrase-level sentiment analysis. In HLT’05, 347–354.
17. Wilson, T.; Wiebe, J.; and Hoffmann, P. 2005b. Recognizing contextual polarity in phrase-level sentiment analysis. In HLT’05, 347–354.
18. Xu, W.; Liu, X.; and Gong, Y. 2003. Document clustering based on non-negative matrix factorization. In SIGIR’03, 267–273.