Feature Weighting Based on Inter-Category and Intra-Category Strength for Twitter Sentiment Analysis

Applied Sciences

Yili Wang¹  Heeyong Youn² 

[1] College of Information and Communication Engineering, Sungkyunkwan University, Suwon 440-746, Korea;College of Software, Sungkyunkwan University, Suwon 440-746, Korea;

关键词: Twitter sentiment analysis;    feature weighting;    opinion mining;    category distribution;   

DOI  :  10.3390/app9010092

来源: DOAJ

【 摘 要 】

The rapid growth in social networking services has led to the generation of a massivevolume of opinionated information in the form of electronic text. As a result, the research on textsentiment analysis has drawn a great deal of interest. In this paper a novel feature weighting approachis proposed for the sentiment analysis of Twitter data. It properly measures the relative significanceof each feature regarding both intra-category and intra-category distribution. A new statistical modelcalled Category Discriminative Strength is introduced to characterize the discriminability of thefeatures among various categories, and a modified Chi-square (2)-based measure is employed tomeasure the intra-category dependency of the features. Moreover, a fine-grained feature clusteringstrategy is proposed to maximize the accuracy of the analysis. Extensive experiments demonstrate thatthe proposed approach significantly outperforms four state-of-the-art sentiment analysis techniquesin terms of accuracy, precision, recall, and F1 measure with various sizes and patterns of training andtest datasets.

【 授权许可】

Unknown