A method of feature selection and sentiment similarity for Chinese micro-blogs

This paper proposes a new sentiment similarity technique to analyse Chinese micro-blog accounts and uses Karhunen–Loéve transform technique and average precision between positive and negative features to measure sentiment similarity.

Abstract

Sentiment analysis of messages posted on micro-blogs is helpful in determining the current usefulness and acceptability of target products or services. It is the basis for finding users with similar attitudes. In this paper, we propose a new sentiment similarity technique to analyse Chinese micro-blog accounts. However, the Chinese text features have not been well studied. Therefore, we first chose four types of feature sets and selected principle features by combining information gain and support vector machine techniques. Next, we compared the four types of features to determine which type of feature contributed more than others. Here we used three classification techniques: decision tree, support vector machines and naive Bayes. Finally, we used Karhunen–Loéve transform technique and average precision between positive and negative features to measure sentiment similarity. Experiment evaluations demonstrated that this new method is efficient and performs better than original average distance for Chinese micro-blogs.