Multi-label emotion recognition of weblog sentence based on Bayesian networks

An increasing number of common users, in the Internet age, tend to express their emotions on the Web about everything they like or dislike. As a consequence, the number of all kinds of reviews, such as weblogs, production reviews, and news reviews, grows rapidly. This makes it difficult for people to understand the opinions of the reviews and obtain useful emotion information from such a huge number of reviews. Many scientists and researchers have attached more attention to emotion analysis of online information in the natural language processing field. Different from previous works, which just focused on the single-label emotion analysis, this paper takes into account rich and delicate emotions and gives special regard to multi-label emotion recognition for weblog sentences based on the Chinese emotion corpus (Ren-CECps). Using the theory of Bayesian networks and probabilistic graphical model, the latent emotion variable and topic variable are employed to find out the complex emotions of weblog sentences. Our experimental results on the multi-label emotion topic model demonstrate the effectiveness of the model in recognizing the polarity of sentence emotions. © 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.