A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
WordNet: a lexical database for English
Communications of the ACM
Data & Knowledge Engineering - Special issue on NLDB '99: applications of natural language to information systems
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Incorporating non-local information into information extraction systems by Gibbs sampling
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Text categorization from category name via lexical reference
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Dictionary based sparse representation for domain adaptation
Proceedings of the 21st ACM international conference on Information and knowledge management
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News articles have always been a prominent force in the formation of a company's financial image in the minds of the general public, especially the investors. Given the large amount of news being generated these days through various websites, it is possible to mine the general sentiment of a particular company being portrayed by media agencies over a period of time, which can be utilized to gauge the long term impact on the investment potential of the company. However, given such a vast amount of news data, we need to first separate corporate news from other kinds namely, sports, entertainment, science & technology, etc. We propose a system which takes news as, checks whether it is of corporate nature, and then identifies the polarity of the sentiment expressed in the news. The system is also capable of distinguishing the company/organization which is the subject of the news from other organizations which find mention, and this is used to pair the sentiment polarity with the identified company.