Improving blog polarity classification via topic analysis and adaptive methods
Document Type
Conference Proceeding
Publication Date
12-1-2010
Abstract
In this paper we examine different linguistic features for sentimental polarity classification, and perform a comparative study on this task between blog and review data. We found that results on blog are much worse than reviews and investigated two methods to improve the performance on blogs. First we explored information retrieval based topic analysis to extract relevant sentences to the given topics for polarity classification. Second, we adopted an adaptive method where we train classifiers from review data and incorporate their hypothesis as features. Both methods yielded performance gain for polarity classification on blog data. © 2010 Association for Computational Linguistics.
Publication Source (Journal or Book title)
NAACL HLT 2010 - Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference
First Page
309
Last Page
312
Recommended Citation
Liu, F., Wang, D., Li, B., & Liu, Y. (2010). Improving blog polarity classification via topic analysis and adaptive methods. NAACL HLT 2010 - Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Proceedings of the Main Conference, 309-312. Retrieved from https://repository.lsu.edu/ag_exst_pubs/502