TAILIEUCHUNG - Báo cáo khoa học: "A study of Information Retrieval weighting schemes for sentiment analysis"

Most sentiment analysis approaches use as baseline a support vector machines (SVM) classifier with binary unigram weights. In this paper, we explore whether more sophisticated feature weighting schemes from Information Retrieval can enhance classification accuracy. We show that variants of the classic scheme adapted to sentiment analysis provide significant increases in accuracy, especially when using a sublinear function for term frequency weights and document frequency smoothing. | A study of Information Retrieval weighting schemes for sentiment analysis Georgios Paltoglou University of Wolverhampton Wolverhampton United Kingdom Mike Thelwall University of Wolverhampton Wolverhampton United Kingdom Abstract Most sentiment analysis approaches use as baseline a support vector machines SVM classifier with binary unigram weights. In this paper we explore whether more sophisticated feature weighting schemes from Information Retrieval can enhance classification accuracy. We show that variants of the classic scheme adapted to sentiment analysis provide significant increases in accuracy especially when using a sublinear function for term frequency weights and document frequency smoothing. The techniques are tested on a wide selection of data sets and produce the best accuracy to our knowledge. 1 Introduction The increase of user-generated content on the web in the form of reviews blogs social networks tweets fora etc. has resulted in an environment where everyone can publicly express their opinion about events products or people. This wealth of information is potentially of vital importance to institutions and companies providing them with ways to research their consumers manage their reputations and identify new opportunities. Wright 2009 claims that for many businesses online opinion has turned into a kind of virtual currency that can make or break a product in the marketplace . Sentiment analysis also known as opinion mining provides mechanisms and techniques through which this vast amount of information can be processed and harnessed. Research in the field has mainly but not exclusively focused in two subproblems detecting whether a segment of text either a whole document or a sentence is subjective or objective . contains an expression of opinion and detecting the overall polarity of the text . positive or negative. Most of the work in sentiment analysis has focused on supervised learning .

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