Bias‐Sentiment‐Topic model for microblog sentiment analysis

Unified models of sentiment and topic have been widely employed in unsupervised sentiment analysis, where each word in text carries both sentiment and topic information. In fact, however, some words tend to express objective things while others prefer to express subjective sentiments. Based on this observation, the concept of word bias is put forward firstly, including objective bias and subjective bias. Considering the relations of bias, sentiment, and topic, a unified framework named Bias‐Sentiment‐Topic (BST) model is then presented to jointly model them for microblog sentiment analysis. After that, an improved Gibbs sampler is proposed for the inference of BST by introducing the general Pólya urn model, which incorporates word embedding as the general knowledge. Finally, experiments on standard test datasets illustrate major improvements of BST in sentiment classification and its effectiveness in separation of words with different biases.