Identifying Negative Sentiment with Sentiment Based LDA and Support Vector Machine Classification
Jun-Hui Zheng, Gang Li · International Journal of Control and Automation · 2016
With the increasing development of online collaborative platforms, there emerge massive subjective texts. However, due to the massive negative news about eroticism, violence, extremity and corruption, as well as the influences of agitators and provocateurs, it is quite likely that Internet users can be turned from conscious individuals into unconscious groups, which contributes to the accumulation of public negative sentiment. In this work, we focus on the identification of sentiment and especially negative sentiment. Specifically, we introduce sentiment layer to the basic LDA topic model to map the texts into a lower dimensional space of topics and sentiment. Besides, we also consider the sentiment dictionary based sentiment feature word extraction method. By feeding the feature words into Support Vector Machine (SVM) classifier, we get the sentiment tendency of texts. Our experiments prove the efficiency of proposed method.