Extended LDA Based Topic Analysis for Big Sentiment Data

Abstract

Owing to the massive emergence of multi-modal data related to big sentiment events, the topic analysis of the text cannot accurately reflect the topic distribution of the whole data set. There are some problems such as semantic deficiency and difficulty in integration. Therefore, the extended LDA(eLDA) based topic analysis for big sentiment data is posed. Firstly, the semantic analysis of text and image data is carried out by probability generation method respectively. The semantic correlation between multi-modal data is used for visual topic learning, a visual topic model is established, and the mapping between visual data and text topics is realized. Furthermore, the fusion of text, audio, video and image data is realized, and multimodal theme analysis is carried out accordingly. The experimental results show that the multimodal topic analysis method can effectively obtain the subject words related to the semantics of big sentiment data. The effect of the subject words tracked by the multi-modal data analysis method is better than that of the single text subject words extraction, which can provide basis for emotional analysis of big sentiment events.