A Sentiment Polarity Categorization Technique for Online Product Reviews

Abstract

Sentiment analysis is also known as opinion mining which shows the people's opinions and emotions about certain products or services. The main problem in sentiment analysis is the sentiment polarity categorization that determines whether a review is positive, negative or neutral. Previous studies proposed different techniques, but still there are some research gaps, i) some studies include only 3 sentiment classes: positive, neutral and negative, but none of them considered more than 3 classes ii) sentiment polarity features were considered on individual basis but none of them considered on both individual and on combined basis iii) No previous technique considered five sentiment classes with 3 sentiment polarity features such as a verb, adverb, adjective and their combinations. In this study, we propose a sentiment polarity categorization technique for a large data set of online reviews of Instant Videos. A comprehensive data set of five hundred thousand online reviews is used in our research. There are five classes (Strongly Negative, Negative, Neutral, Positive and Strongly Positive). We also consider three polarity features Verb, Adverb, Adjective and their combinations with their different senses in review-level categorization. Our experiments for review-level categorization show promising outcomes as the accuracy of our results is 81 percent which is 3 percent better than many previous techniques whose average accuracy is 78 percent. INDEX TERMS Sentiment, opinion mining, social media, natural language processing.

Opinion Mining of Consumer Reviews Using Deep Neural Networks with Word-Sentiment Associations

“…Following [26], we extracted the unigrams, bigrams and sentiment patterns. • Support vector machine with word sense disambiguation (SVM-WSD) [27] uses adverbs scored using the SentiWordNet lexicon as input features. Thus, positive and negative scores were assigned to adverbs, and SVM was trained using the LibLINEAR library.…”

A hybrid sentiment analysis approach using black widow optimization based feature selection

“…SA methods are mainly classified as lexicon-based approach, ML based approach and hybrid approach (Kausar et al, 2020). In the lexicon-based approach, the polarities and the frequencies of the negative and positive words are examined to get the sentiment of the analyzed text using a predefined dictionary of words (Bollegala et al, 2013, Cho et al, 2014.…”

“…SA is mainly classified as lexicon-based approach, machine learning (ML) approach and hybrid approach (Kausar et al, 2020). The sentiment polarity of dataset is calculated with the words' semantic orientation in lexicon-based approach (Taboada et al, 2011).…”

A Novel Sentiment Analysis for Amazon Data with TSA based Feature Selection

“…Based on the mechanisms used, SA is mainly classified under three forms like machine learning (ML), lexicon-based and hybrid [2]. In ML-based approaches, various learning algorithms and labelled datasets are utilized to train the classifier for identifying the sentiments [3].…”