Sentiment Analysis for Amazon Product Reviews
Apoorva Verma1, Chirag Rawat2, Shilpy Gupta3
1Apoorva Verma, Department of Computer Science, Galgotias University, Gautam Buddha Nagar (U.P), India.
2Chirag Rawat, Department of Computer Science, Galgotias University, Gautam Buddha Nagar (U.P), India.
3Mrs. Shilpy Gupta, Department of Computer Science, Galgotias University, Gautam Buddha Nagar (U.P), India.
Manuscript received on 11 June 2022 | Revised Manuscript received on 02 July 2022 | Manuscript Accepted on 15 July 2022 | Manuscript published on 30 July 2022 | PP: 109-112 | Volume-11 Issue-2, July 2022 | Retrieval Number: 100.1/ijrte.B70990711222 | DOI: 10.35940/ijrte.B7099.0711222
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Sentiment analysis is a classicfication process whereby machine learning techniques are applied on text-driven datasets in order to analyse the emotion / opinion expressed in a text, e.g. a message being positive or negative about a certain topic. The problem is to conduct a sentiment analysis (positive and negative sentiment) on online product reviews of Products (unlocked mobile phones) sold on Amazon.com. The trained model can be used to predict users’ sentiment based on their online reviews. In this project, different machine learning algorithms are compared, trained and tested on a dataset containing 400000 reviews. The performance of three different algorithms were compared: Multinomial Naive Bayes (MNB), Logistic Regression and Long short-term memory network (LSTM). The Logistic Regression model resulted in the highest performance with Accuracy of 0.95 and AUC of 0.94. The dataset consists of 400 thousand reviews of products (unlocked mobile phones) sold on Amazon.com which is publicly available on Kaggle. Solution to the problem would be useful for a brand to gain a broad sense of user’s’ sentiment towards a product through online reviews Further study is needed to investigate if the classfication remains accurate when including more than two classes (e.g. Introducing a neutral class).
Keywords: Sentiment Analysis (Positive and Negative sentiment), Unlocked Mobile Phones, online product reviews, Amazon
Scope of the Article: Sentiment Analysis