Sentiment Extraction From Unstructured Text Using Tabu Search-Enhanced Markov Blanket

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Sentiment Extraction From Unstructured Text Using Tabu Search-Enhanced Markov Blanket (2004)

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by Xue Bai , Rema Padman , Edoardo Airoldi

Venue:	In Proceedings of the Workshop on Mining the Semantic Web, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Citations:	15 - 4 self

BibTeX

@INPROCEEDINGS{Bai04sentimentextraction,
    author = {Xue Bai and Rema Padman and Edoardo Airoldi},
    title = {Sentiment Extraction From Unstructured Text Using Tabu Search-Enhanced Markov Blanket},
    booktitle = {In Proceedings of the Workshop on Mining the Semantic Web, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
    year = {2004},
    pages = {24--35},
    publisher = {Springer-Verlag}
}

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Abstract

Abstract. Extracting sentiments from unstructured text has emerged as an important problem in many disciplines. An accurate method would enable us, for example, to mine on-line opinions from the Internet and learn customers ’ preferences for economic or marketing research, or for leveraging a strategic advantage. In this paper, we propose a two-stage Bayesian algorithm that is able to capture the dependencies among words, and, at the same time, finds a vocabulary that is efficient for the purpose of extracting sentiments. Experimental results on the Movie Reviews data set show that our algorithm is able to select a parsimonious feature set with substantially fewer predictor variables than in the full data set and leads to better predictions about sentiment orientations than several state-of-the-art machine learning methods. Our findings suggest that sentiments are captured by conditional dependence relations

Keyphrases

sentiment extraction experimental result predictor variable parsimonious feature unstructured text two-stage bayesian algorithm on-line opinion sentiment orientation important problem strategic advantage learn customer preference marketing research movie review data conditional dependence relation many discipline accurate method full data set several state-of-the-art machine

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