Feature Specific Sentiment Analysis for Product Reviews

TL;DR

This paper introduces a novel dependency parsing-based method for feature-specific sentiment analysis in product reviews, effectively extracting opinions about different features with minimal data requirements.

Contribution

It presents a new approach that combines dependency parsing and graph partitioning to identify feature-specific opinions in reviews, outperforming existing methods with less data.

Findings

High accuracy across multiple domains

Performs comparably to state-of-the-art systems

Requires minimal data for domain-independent parameters

Abstract

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.