Embedding-based Feature Extraction Methods for Chinese Sentiment Classification

Sentiment analysis, also known as opinion mining, seeks to figure out points of view from documents. Sentiment classification is a specific task of sentiment analysis that divides documents into positive and negative sentiment polarities according to the attitudes expressed. Feature extraction is a significant part of sentiment classification. Traditional feature extraction methods mine statistical information in documents but neglect semantic relationships between words, while some embedding methods successfully capture semantics but have difficulty in distinguishing sentiment polarities. In this paper, we propose three different sentiment specific models that take advantages of the statistical information in a document as well as the semantic relationships between words. Our models shed more light on the different roles of words in documents, assigning them different weights. Experimental results on three Chinese datasets illustrate that, in general, our models are superior to other models and provide state-of-the-art performance. Our models are efficient and have high stability.