Extracting Aspect Specific Sentiment Expressions Implying Negative Opinions

Subjective expression extraction is a central problem in fine-grained sentiment analysis. Most existing works focus on generic subjective expression extraction as opposed to aspect specific opinion phrase extraction. Given the ever-growing product reviews domain, extracting aspect specific opinion phrases is important as it yields the key product issues that are often mentioned via phrases (e.g., "signal fades very quickly," "had to flash the firmware often"). In this paper, we solve the problem using a combination of generative and discriminative modeling. The generative model performs a first level processing facilitating (1) discovery of potential head aspects containing issues, (2) generation of a labeled dataset of issue phrases, and (3) feed latent semantic features to subsequent discriminative modeling. We then employ discriminative large-margin and sequence modeling with pivot features for issue sentence classification and issue phrase boundary extraction. Experimental results using real-world reviews from Amazon.com demonstrate the effectiveness of the proposed approach.