Abstract
Extracting the latent structure of the aspects and the sentiment polarities is important as it helps customers to understand people' preference to a certain product and show the reasons why they prefer this product. However, insufficient studies have been done to effectively reveal the structure sentiment of the aspects from short texts due to the shortness and sparsity. In this paper, we propose a structured sentiment analysis (SSA) approach to understand the sentiments and opinions expressed by people in short texts. The proposed SSA approach has three advantages: (1) automatically extracts a hierarchical tree of a product's hot aspects from short texts; (2) hierarchically analyses people's opinions on those aspects; and (3) generates a summary and evidences of the results. We evaluate our approach on popular products. The experimental results show that the proposed approach can effectively extract a sentiment tree from short texts.