Opinion Mining Based Entity Ranking using Fuzzy Logic Algorithmic Approach

TL;DR

This paper introduces a fuzzy logic-based method for opinion mining at a granular level, enabling more precise entity ranking based on sentiment analysis of evaluative statements.

Contribution

It proposes a novel approach combining opinion mining with fuzzy logic reasoning to improve entity ranking accuracy at a finer granularity.

Findings

Effective extraction of opinions at a deeper granularity

Improved entity ranking based on sentiment analysis

Demonstrated the approach's potential on real-world data

Abstract

Opinions are central to almost all human activities and are key influencers of our behaviors. In current times due to growth of social networking website and increase in number of e-commerce site huge amount of opinions are now available on web. Given a set of evaluative statements that contain opinions (or sentiments) about an Entity, opinion mining aims to extract attributes and components of the object that have been commented on in each statement and to determine whether the comments are positive, negative or neutral. While lot of research recently has been done in field of opinion mining and some of it dealing with ranking of entities based on review or opinion set, classifying opinions into finer granularity level and then ranking entities has never been done before. In this paper method for opinion mining from statements at a deeper level of granularity is proposed. This is done by using fuzzy logic reasoning, after which entities are ranked as per this information.