Open AccessProduct Sentiment Analysis for Amazon Reviews
Author(s) -
Arwa S. M. AlQahtani
Publication year - 2021
Publication title -
international journal of computer science and information technology/international journal of computer science and information technology (chennai. print)
Language(s) - English
Resource type - Journals
eISSN - 0975-4660
pISSN - 0975-3826
DOI - 10.5121/ijcsit.2021.13302
Subject(s) - computer science , sentiment analysis , naive bayes classifier , artificial intelligence , machine learning , random forest , purchasing , support vector machine , product (mathematics) , recall , natural language processing , marketing , geometry , mathematics , business , linguistics , philosophy
Recently, Ecommerce has Witnessed Rapid Development. As A Result, Online Purchasing has grown, and that has led to Growth in Online Customer Reviews of Products. The Implied Opinions in Customer Reviews Have a Massive Influence on Customer's Decision Purchasing, Since the Customer's Opinion About the Product is Influenced by Other Consumers' Recommendations or Complaints. This Research Provides an Analysis of the Amazon Reviews Dataset and Studies Sentiment Classification with Different Machine Learning Approaches. First, the Reviews were Transformed into Vector Representation using different Techniques, I.E., Bag-Of-Words, Tf-Idf, and Glove. Then, we Trained Various Machine Learning Algorithms, I.E., Logistic Regression, Random Forest, Naïve Bayes, Bidirectional Long-Short Term Memory, and Bert. After That, We Evaluated the Models using Accuracy, F1-Score, Precision, Recall, and Cross-Entropy Loss Function. Then, We Analyized The Best Performance Model in Order to Investigate Its Sentiment Classification. The Experiment was Conducted on Multiclass Classifications, Then we Selected the Best Performing Model And Re-Trained It on the Binary Classification.