Affiliations: [a]
School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China
| [b]
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China
Correspondence:
[*]
Corresponding author. Sheng Tai Zhang, School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing 100876, China. E-mail: sttzz@163.com.
Abstract: Sentiment analysis mainly studies the emotional tendencies of texts from grammar, semantic rules and other aspects. The texts from social network are characterized by less words, irregular grammar, data noise and so on, which have increased the difficulty of emotion analysis. In order to improve the performance of machine learning in sentiment analysis, this study proposed the Majority Decision Algorithm to classify the emotional tendentious of the text in WeChat, combined the characteristics of five classifiers and integrated the classification results of five classifiers, eventually the text can be classified in WeChat. Firstly, this study utilized the BlueStacks to crawl the cache of WeChat Moment developed by Tencent company. Secondly, the cache was processed by Python to get the WeChat dataset. After the Chinese word segmentation, data cleaning and segmentation, the sentiment classification experiment were carried out using different classifiers. Finally, a Majority Decision Algorithm composed of five classifiers was established. It included, Naive Bayes (sklearn), Naive Bayes (SnowNLP), SVM (linear), SVM (RBF) and SGD. Then, the comparison was carried out between the performance of the algorithm and the five classifiers. Results show that the precision rates of the five classifiers are 0.8598, 0.8154, 0.8511, 0.8739 and 0.8678; the recall rates are 0.8544, 0.8482, 0.9380, 0.9226 and 0.9349; F1 scores are 0.8571, 0.8315, 0.8924, 0.8975 and 0.9001, respectively. The algorithm of the Precision rate, Recall rate and F1 score were 0.8804, 0.9349 and 0.9069, respectively, indicating that algorithm in current study significantly improved the performance, which can be effectively applied into the new text form of WeChat Moment. The study can provide theoretical reference for sentiment classification of Chinese text based on machine learning.
