Amazon product sentiment analysis using RapidMiner

Abstract

Nowadays, online reviews from customers have created significance for any business especially when it comes to Amazon website. This research predicts the customer reviews based on three main categories; health and beauty, toys and games and electronics. The reviews are classified whether as positive, negative, or neutral. Sentiment Analysis is a data analysis concept in which a collection of reviews is considered, and those reviews are analyzed, processed, and recommended to the user. The dataset use in this research is collected from the Dataworld website. The research presented in this paper was carried out initially; the reviews must be pre-processed in order to remove the unwanted data before being converted from text to vector representation using a range of feature extraction techniques such as TF-IDF. After that, the dataset is classified using Naive Bayes, Decision Tree and Random Forest algorithms. The accuracy, precision and recall were implemented as performance measures in order to evaluate the performance sentiment classification for the given reviews. The result shows that Decision Tree is the best classifier with the highest accuracy for the health and beauty, and electronic categories. For the toys and games category, the best classifier with the highest accuracy is Random Forest.