Impact analysis of adverbs for sentiment classification on Twitter product reviews

Abstract

Social networking websites such as Twitter provide a platform where users share their opinions about different news, events, and products. A recent research has identified that 81% of users search online first before purchasing products. Reviews are written in natural language and needs sentiment analysis for opinion extraction. Various approaches have been proposed to perform sentiment classification based on polarity bearing words in reviews such as noun, verb, adverb, and an adjective. Prior researchers have also identified the role of an adverb as a feature. However, impact analysis of adverb forms, are not yet studied and remains an open research area. This study focused on the following tasks: (1) impact of different forms of adverbs that are not studied for sentiment classification; (2) analysis of possible combinations of eight forms that are 255. The different forms are Adverb (RA), Degree Adverbs (RG), Degree Comparative Adverbs (RGR), General Adverbs (RR), General Comparative Adverbs (RRR), Locative Adverbs (RL), Prep. Adverb (RP), and Adverbs of time (RT); (3) comparison with benchmark dataset. Dataset of 5513 tweets is used to evaluate the idea. The findings of this work show that RRR and RR are important polarities bearing words for neutral opinions, RL for positive, and RP for negative opinions.