Feature Specific Sentiment Analysis for Product Reviews
This work addresses the challenge of fine-grained sentiment analysis for product reviews, which is incremental as it builds on existing methods but introduces a novel approach to handle domain-independent parameters with limited data.
The paper tackles the problem of identifying feature-specific opinion expressions in product reviews with mixed emotions by using dependency parsing and graph partitioning to extract and merge closely associated opinion words, achieving high accuracy across domains and performing on par with state-of-the-art systems despite minimal data requirements.
In this paper, we present a novel approach to identify feature specific expressions of opinion in product reviews with different features and mixed emotions. The objective is realized by identifying a set of potential features in the review and extracting opinion expressions about those features by exploiting their associations. Capitalizing on the view that more closely associated words come together to express an opinion about a certain feature, dependency parsing is used to identify relations between the opinion expressions. The system learns the set of significant relations to be used by dependency parsing and a threshold parameter which allows us to merge closely associated opinion expressions. The data requirement is minimal as this is a one time learning of the domain independent parameters. The associations are represented in the form of a graph which is partitioned to finally retrieve the opinion expression describing the user specified feature. We show that the system achieves a high accuracy across all domains and performs at par with state-of-the-art systems despite its data limitations.