Sentiment Analysis on Amazon Reviews of Mobile Phones using Machine Learning

The world is witnessing a boom in the digital age. Digital shops have literally landed into our homes. Almost any required product can now be purchased online via websites or mobile apps without having to step out. Due to online shopping, many customers rely on online reviews from other customers before making a purchase. Customer reviews are gaining more and more importance as they play a probably vital role in the sale and purchase of a product. Customer reviews also provide firsthand feedback coming directly from the customers themselves; this can benefit even the sellers in improving future sales. Analyzing the reviews can provide probable causes for failure or success of a product. Henceforth, the current paper presents the sentiment analysis of the reviews to better understand the feelings expressed by the customers. The very popular and widely used mobile phones were chosen as the product and Amazon was chosen as the digital seller for the current study. Initially, this work began with data preprocessing. Followed by data preprocessing, Bow and n-grams word embedding have been used to represent the clean reviews in vector representation, and then the features were derived. Finally, the performance of supervised machine learning classifiers such as Decision Tree, Naive Bayes, Random Forest, and SVM was empirically evaluated through accuracy, recall, f1-score, and precision. The results of empirical evaluation revealed that the Random Forest Classifier shows best performance with 97.48% accuracy.