Implementation of Deep Learning Based Sentiment Classification and Product Aspect Analysis

M.D Patil,M D Patil,Shweta Dhabekar,V.A Vyawahare

doi:10.1051/itmconf/20214003032

Abstract

With the increase in E-Commerce businesses in the last decade,the sentiment analysis of product reviews has gained a lot of attention in linguistic research. In literature, the survey depicts the majority of the research done emphasizes on mere polarity identification of the reviews. The proposed system emphasized on classifying the sentiment polarity and the product aspect identification from the reviews. Proposed work experimented with traditional machine learning techniques as well as deep neural networks such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short Term Memory(LSTM) Networks. The proposed system gives a better understanding of these algorithms by comparing the outcomes. The Deep Learning approach in the proposed work successfully provides a mechanism which identifies the review polarity and intensity of the reviews and also analyses the short form words used by people in the reviews. The experimental results in this work, applied on amazon product dataset, shows that the LSTM model works the best for sentiment analysis and intensity of reviews with 93% accuracy. This research work also predicts polarity for short-form word reviews which is the common trend these days while writing the reviews.

Highlights

In recent years, there has been an increase in research activities focused on analyzing sentiment in textual resources
Traditional machine learning mechanisms and deep learning techniques like Recurrent Neural Network (RNN), Convolutional Neural Network (CNN) and LSTM are included in these models
If the person writes the review “the product is gud”, the word “gud” is not a standard dictionary word, so the CNN model is deployed to predict the actual words from the trained dataset, which comprises of actual words and possible short form words that can be used in a days

Summary

Introduction

There has been an increase in research activities focused on analyzing sentiment in textual resources. Sentiment analysis or opinion mining is one of the subtopics of this research, in which it computationally investigates people's views, assessments, attitudes and emotions regarding entities, individuals, situations, events, texts. This approach has a wide range of applications. Yi-Fan and Maria Soledad Elli gathered sentiments by analyzing reviews and evaluated the results to develop a business model in their paper[3]. Authors said that this model gained them a high level of precision. In contrast to this, [8] recommended employing recursive neural networks to gain a better grip over sentiment prediction

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