Fine Granular Aspect Analysis using Latent Structural Models

In this paper, we present a structural learning model for joint sentiment classification and aspect analysis of text at various levels of granularity. Our model aims to identify highly informative sentences that are aspect-specific in online custom reviews. The primary advantages of our model are two-fold: first, it performs document-level and sentence-level sentiment polarity classification jointly; second, it is able to find informative sentences that are closely related to some respects in a review, which may be helpful for aspect-level sentiment analysis such as aspect-oriented summarization. The proposed method was evaluated with 9,000 Chinese restaurant reviews. Preliminary experiments demonstrate that our model obtains promising performance.

• Type: Journal
• Year: 2012
• Where: ACL
• Authors: Lei Fang, Minlie Huang
• Keywords: ACL 2012, Computational Linguistics, Document Level, Promising Performance, Sentiment Analysis