Quantifying modified opinion strength: A fuzzy inference system for Sentiment Analysis

A fuzzy inference system based on experimentally designed fuzzy membership functions and concepts of hedges to standardized and formulate the process of strength quantification of subjective sentences when strength of opinion word get modified by the presence of n-gram adverbial modifiers pattern in the sentence.

Abstract

In Sentiment Analysis or Opinion Mining, automatic quantification of the strength of opinion, expressed on any feature is very important task. However, it is quite challenging to automatically quantify the strength of opinion words, whenever they get modified by adverbial modifiers. For example the intensity of an opinion word “beautiful” gets increased in “very beautiful”, gets decreased in “slightly beautiful” and complemented in “not beautiful”. In our work, we have designed a fuzzy inference system based on experimentally designed fuzzy membership functions and concepts of hedges to standardized and formulate the process of strength quantification of subjective sentences when strength of opinion word get modified by the presence of n-gram adverbial modifiers pattern in the sentence. Using our membership functions and considering the manually quantified opinion strength for n-gram adverbial modifiers pattern as a baseline, we observed that our fuzzy inference system is producing output values with a very small average root mean square error with the targeted baseline points.