Subjectivity Classification using Machine Learning Techniques for Mining Feature-Opinion Pairs from Web Opinion Sources

TL;DR

This paper presents a hybrid machine learning and rule-based approach to extract feature-opinion pairs from web reviews, improving the accuracy of opinion mining by filtering out factual sentences.

Contribution

It introduces a combined supervised learning and linguistic rule-based method for more precise feature-opinion pair extraction from subjective customer reviews.

Findings

Effective classification of subjective vs. objective sentences achieved

Improved extraction of relevant feature-opinion pairs demonstrated

Method validated on electronic product reviews

Abstract

Due to flourish of the Web 2.0, web opinion sources are rapidly emerging containing precious information useful for both customers and manufactures. Recently, feature based opinion mining techniques are gaining momentum in which customer reviews are processed automatically for mining product features and user opinions expressed over them. However, customer reviews may contain both opinionated and factual sentences. Distillations of factual contents improve mining performance by preventing noisy and irrelevant extraction. In this paper, combination of both supervised machine learning and rule-based approaches are proposed for mining feasible feature-opinion pairs from subjective review sentences. In the first phase of the proposed approach, a supervised machine learning technique is applied for classifying subjective and objective sentences from customer reviews. In the next phase, a rule based method is implemented which applies linguistic and semantic analysis of texts to mine feasible feature-opinion pairs from subjective sentences retained after the first phase. The effectiveness of the proposed methods is established through experimentation over customer reviews on different electronic products.