Comparative Opinion Mining: A Review

TL;DR

This paper reviews the specific subfield of comparative opinion mining, focusing on techniques, elements, preprocessing tools, and datasets, highlighting its importance in evaluating and comparing opinions in text.

Contribution

It is the first comprehensive review dedicated solely to comparative opinion mining, covering methods, elements, preprocessing, and datasets used in the field.

Findings

Provides a structured overview of techniques used in comparative opinion mining.

Summarizes key elements involved in extracting comparative opinions.

Lists datasets and preprocessing tools relevant to the field.

Abstract

Opinion mining refers to the use of natural language processing, text analysis and computational linguistics to identify and extract subjective information in textual material. Opinion mining, also known as sentiment analysis, has received a lot of attention in recent times, as it provides a number of tools to analyse the public opinion on a number of different topics. Comparative opinion mining is a subfield of opinion mining that deals with identifying and extracting information that is expressed in a comparative form (e.g.~"paper X is better than the Y"). Comparative opinion mining plays a very important role when ones tries to evaluate something, as it provides a reference point for the comparison. This paper provides a review of the area of comparative opinion mining. It is the first review that cover specifically this topic as all previous reviews dealt mostly with general opinion mining. This survey covers comparative opinion mining from two different angles. One from perspective of techniques and the other from perspective of comparative opinion elements. It also incorporates preprocessing tools as well as dataset that were used by the past researchers that can be useful to the future researchers in the field of comparative opinion mining.