关键词: 社交媒体, 情感倾向性, 动态情感词典, 特征筛选, 特征分析

Abstract: In this paper, we address the problem of text emotional orientation analysis on public opinion for diverse participants and dynamic changeable emotional s in different domain social media. To solve this problem, our research is based on Static Emotional Dictionary (SED), and provides a new scheme for Dynamic Domain Sentiment Dictionary (DDSD) derived from significant differences of mutual information between Pos/Neg emotional categories for each word, as well as its threshold’s experimental evaluation, for different domain social media. Next, according to the principle of likelihood estimate of statistical distribution of Pos/Neg training corpus, we propose a Feature Selection Algorithm with Evolutionary Structural Optimization method (FSA-ESO), which chooses optimal features from part-of-speech, polar words and statistical characteristic of words. By using this algorithm, the optimal combination of features can be experimentally evaluated from the different domain social media. Finally, to validate such a combination of features, the experimental comparisons of different classification algorithms are made over several domain social media, respectively. The experiment’s results indicate that our proposed method is applicable to different domain social media and get better performance for text emotional orientation classification.

Key words: social media, emotional orientation, dynamic sentiment dictionary, feature selection, feature analysis