Sentiment polarity classification is perhaps the most widely studied topic. It classifies an opinionated document as expressing a positive or negative opinion. In this paper, using movie review dataset, we perform a comparative study with different single kind linguistic features and the combinations of these features. We find that the classic topic-based classifier(Naive Bayes and Support Vector Machine) do not perform as well on sentiment polarity classification. And we find that with some combination of different linguistic features, the classification accuracy can be boosted a lot. We give some reasonable explanations about these boosting outcomes.
@cite_13 work on polarity classification is perhaps the closest to ours. They explored some polarized features, transition features and some combinations with unigram. He applied maximum entropy classifier to evaluate those features by accuracy. In contrast, we utilize the contextual information of trigram and the effectiveness of adjective and adverb words, which we combine polarized features with both unigram and adjective adverb trigram features. And we give an overall evaluation based on accuracy in different document representations and two classic classifiers.
@cite_13
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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.