Abstract
Most existing unsupervised approaches to detect topic sentiment in social texts consider only the text sequences in corpus and put aside social dynamics, as leads to algorithm’s disability to discover true sentiment of social users. To address the issue, a probabilistic graphical model LDTSM (Long-term Dependence Topic-Sentiment Mixture) is proposed, which introduces dependency distance and uses the dynamics of social media to achieve the perfect combination of inheriting historical topic sentiment and fitting topic sentiment distribution underlying in current social texts. Extensive experiments on real-world SinaWeibo datasets show that LDTSM significantly outperforms JST, TUS-LDA and dNJST in terms of sentiment classification accuracy, with better inference convergence, and topic and sentiment evolution analysis results demonstrate that our approach is promising.
Highlights
With the rapid development of social media and intelligent mobile devices, the public can conveniently express their opinions and share their feelings regarding social, economic or political issues through online social platforms like Twitter and Facebook
EFFICIENCY ANALYSIS OF long-term time-dependent topic sentiment model (LDTSM) we investigate efficiency of LDTSM, by comparing running time of LDTSM and the other three probabilistic graphical models, i.e., JST, dNJST and TUS-LDA
In this paper, we have presented a new topic-sentiment modelling framework with long-term dependency for both sentiment classification and topic extraction
Summary
With the rapid development of social media and intelligent mobile devices, the public can conveniently express their opinions and share their feelings regarding social, economic or political issues through online social platforms like Twitter and Facebook. Generative models are proposed to model the joint distribution of sentiment and topic with parameters that can be interpreted as reflecting latent associations between topics and sentiments in the data. Most of the sliding-window-based sentiment analysis approaches take an assumption that sentiment patterns in different time windows are independent and identically distributed. When a typical generative model, such as JST [4] or TSM [5], is chosen to estimate sentiment distribution of Twitter posts in a given time window, it may not achieve satisfactory results. Motivated by natural immediacy of social media and subtle dependency between text topics and text sentiments, and based on assumptions that 1) topics of texts are generated according to sentiment distributions of texts; 2) roughly similar is sentiment and topic conveyed in social media content posted by the same user over adjacent periods of time.
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