Comparative Performance of Machine Learning Techniques for Sentiment and Insight Analysis in Amazon Product Reviews
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V4I1P106Keywords:
Sentiment Analysis, TF-IDF, Amazon Product Reviews, Consumer Behavior, Machine LearningAbstract
The sentiment analysis of online product review has been a pressing concern in perception and support of sound decision-making using information provided by e-commerce portals. Through a random forest (RF) classifier and a sophisticated preprocessor pipeline, this paper proposes a useful sentiment classification model of Amazon product reviews. The data collection is made in the beginning of the research process with Amazon Reviews (2018). It is pre-processed in several stages, such as special character removal, stop-word removal, tokenization and part-of-speech (POS) tagging to normalize and standardize the textual data. TF-IDF is an extraction tool which converts text into meaningful numerical processes. RF model is then trained to predict customer sentiment and its ensemble structure is employed to overfitting and maximize predictive stability. The measures of performance in the model include accuracy, precision, recall, F1-score and loss. RF classifier is an excellent accuracy (ACC) rate of 98.83, a high precision (PRE) rate of 97.70, a high recall (REC) rate of 98.64, and a balanced F1-score (F1) rate of 98.17 compared to other traditional and deep learning classifiers such as the Logistic Regression, SVM, NB-SVM, and CNN-RNN. Overall, the RF-based sentiment analysis model is a highly trustworthy, scalable and efficient way of obtaining insights using a huge volume of e-commerce reviews data.
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