Corporate News Classification and Valence Prediction: A Supervised Approach

Proceedings of the Annual Meeting of the Association for Computational Linguistics

Volumn , Issue , 2011, Pages 175-181

  Haider, Syed Aqueel ^a   Mehrotra, Rishabh ^b  

^a MANIPAL INSTITUTE OF TECHNOLOGY   (India)

^b BIRLA INSTITUTE OF TECHNOLOGY AND SCIENCE   (India)

Author keywords

[No Author keywords available]

Indexed keywords

CORPORATES; GENERAL PUBLICS; LARGE AMOUNTS; LONG-TERM IMPACTS; MEDIA AGENCIES; NEWS ARTICLES; SCIENCE TECHNOLOGIES;

EID: 84871100020     PISSN: 0736587X     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. A New Text Mining Approach Based on HMM-SVM for Web News ClassificationLewis, D. D.: Reuters- 21578 Document Corpus V1.0.
2. Angela K. Davis, Jeremy M. Piger, and Lisa M. Sedor. 2006.Beyond the numbers: An analysis of optimistic and pessimistic language in earnings press releases. Technical report, Federal Reserve Bank of St Louis.
3. B. E. Boser, I. Guyon, and V. Vapnik. A training algorithm for optimal margin classiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pages 144{152. ACM Press, 1992.
4. Barak and Dagan.2009. Text Categorization from Category Name via Lexical Reference. Proceedings of NAACL HLT 2009.
5. Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm http://acl.ldc.upenn.edu/J/J93/J93-2004.pdf.
6. Devitt et al, (2007) Sentiment Polarity Identification in Financial News: A Cohesion-based Approach.
7. George A. Miller (1995). WordNet: A Lexical Database for English.Communications of the ACM Vol. 38, No. 11: 39-41.
8. Jenny Rose Finkel, Trond Grenager, and Christopher Manning. 2005. Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. Proceedings of the 43nd Annual Meeting of the Association for Computational Linguistics (ACL 2005), pp. 363-370.
9. Kristina Toutanova and Christopher D. Manning. 2000. Enriching the Knowledge Sources Used in a Maximum Entropy Part-of-Speech Tagger. In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC-2000), pp. 63-70.
10. Melville et al.(2009) Sentiment Analysis of Blogs by Combining. Lexical Knowledge with Text Classification.
11. Ozgur, A.: Supervised and Unsupervised Machine Learning Techniques for Text Document Categorization. Master's Thesis (2004), Bogazici University, Turkey.
12. Porter, M.F. (1980) An Algorithm for Suffix Stripping, Program, 14(3): 130-137.
13. Sebastiani, F.: Machine Learning in Automated Text Categorization. ACM Computing Surveys 34 no. 5 (2002).
14. Sentiment Mining in Large News Datasets. Roshan Sumbaly, Shakti Sinha, May 10, 2009.
15. UPAR7: A knowledge-based system for headline sentiment tagging. François-Régis Chaumartin Lattice/Talana - Université Paris 7.
16. U. Hahn and M. Romacker Content Management in the SynDiKATe system - How t documents are automatically transformed to text knowledge bases. Data & Knowledge E ing, 35, 2000, pages 137-159.
17. Victor Niederhoffer. 1971. The analysis of world events and stock prices. Journal of Business, 44(2):193-219.