SUIT: A Supervised User-Item Based Topic Model for Sentiment Analysis

• DOI: 10.1609/aaai.v28i1.8947
• Publication Date: 2022-06-23T09:49:45Z
• Authors: Fangtao Li; Sheng Wang; Shenghua Liu; Ming Zhang
• Keywords: Sentiment Analysis; Microblogging; Factor (programming language)

Probabilistic topic models have been widely used for sentiment analysis. However, most of existing methods only model the text, but do not consider user, who expresses sentiment, and item, which is expressed on. Since different users may use expressions items, we argue that it better to incorporate user item information into In this paper, propose a new Supervised User-Item based Topic model, called SUIT It can simultaneously utilize textual latent user-item factors. Our proposed method uses tensor outer product text proportion vector, factor label generalization. Extensive experiments are conducted on two datasets: review dataset microblog dataset. The results demonstrate advantages our model. shows significant improvement compared with supervised collaborative filtering methods.