Unfair Review Detection on Amazon Reviews Using Sentiment Analysis

Abstract

AbstractReviews provided by the user have significant value of product within the world of e-market. Individuals and organizations depend heavily on social media now a days for customer reviews in their decision-making on purchases. However, for private gains like profit or fame, people post fake reviews to market or demote certain target products also in order to deceive the reader. To get genuine user experiences and opinions, there’s a requirement to detect such spam or fake reviews. Sentiment Analysis (SA) has been used which is now the topic generating the major attentiveness in the text analysis field. Sentiment classification methods are used on a dataset of user reviews for unfair reviews detection and trend patterns. Various supervised and deep learning approaches like Logistic Regression, Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) are used to determine the overall semantic of customer reviews by classifying them into positive and negative sentiment. The performance of sentiment classification is also evaluated by using accuracy, precision, and recall as performance measures and select the best approach for classification.KeywordsFake reviewSupervised learningSentimental analysisLSTMSVMLogistic regression