单词统计特性在情感词自动抽取和商品评论分类中的作用

he statistical features of words are widely used in Natural Language Processing. This paper summarizes eight types of statistical features, and studies the role of these features in extracting sentimental words and classifying product reviews. Sentiment words extraction result showed that combining these statistical features and PoS tags of words can achieve much higher extraction accuracy than other methods with precision of 76.4%. Product reviews classification results showed that in contrast with sentimental words in constructing the feature space, exclusively using these 8 kinds of statistical features can improve classification precision by 10.8%. Different from the multi-dimensions of lexical elements in the vector space models (VSM) , this paper only employed these 8 types of statistical features in representation of words or documents, which has the ability that can lower the VSMs dimension and can effectively derive the latent semantic space without expensive time and space complexity of SVD calculation.