Assigning Polarity Scores to Reviews Using Machine Learning Techniques
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概要
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We propose a novel type of document classification task that quantifies how much a given document (review) appreciates the target object by using a continuous measure called sentiment polarity score (SP score) rather than binary polarity (good or bad). An SP score gives a concise summary of a review, and provides more information than binary classification. The difficulty of this task lies in the quantification of polarity. In this paper we use support vector regression (SVR) to tackle this problem. Experiments on book reviews using five-point scales show that SVR outperforms a multi-class classification method using support vector machines, and the results are close to human performance. We also examine the effect of sentence subjectivity detection using a Naive Bayes classifier, and show that this improves the robustness of the classifier.本論文では,ある対象を評価している文章(レビュー)が与えられた時,対象物に対する評価が｢良い｣か｢悪い｣かでレビューを二倍分類するのではなく,どの桂度｢良い｣か｢悪い｣かの指標(sentimentpolarityscore (SPscore))をレビューに与える新しいタスクを提案する.SPscoreはレビューの簡潔な要約であり,単純な｢良い｣か｢悪い｣かの二倍分類より詳細な情報を与える.このタスクの難しさは連続した量であるSPscoreをどのようにしてレビューから得られるかにある.本稿ではsupportvectorregressionを用いてSPscoreを求める方法を提案する.5段階評価がついた本に対するレビューを用いた実験で,我々の手法がsupportvectormachinesを用いた多値分類より高い精度であり,人による指標の予測結果に近いことを示す.また,NaiveBayesClassifierを用いた文単位での主観性分析を用いることにより我々の手法の頑健性が増すことを示す.
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