Quantitative Evaluation of Sentiment Analysis on E-Commerce Data

Abstract

With the emergence of technology and Big Data in the modern world, it is important to govern such vast amounts of data. Amazon and other e-commerce websites play a vital role in the delivery of goods services to the user. Nonetheless, such services are frequently accompanied by consumer reviews and ratings of the things they sell. Such reviews and ratings provided in the form of textual feedback serve to improve service delivery and product quality in the event that a consumer is dissatisfied. Thus, the process of evaluations and ratings is regarded as a significant component of the customer satisfaction process. Thus, it is vital to analyse them so that useful insights can be gleaned from them and the correct judgements can be made regarding the enhancement of the product. With emphasis on this concept, the authors of the proposed paper intend to develop a model capable of performing sentiment analysis on customer-provided product reviews and analysing products with positive and negative comments. On the basis of these reviews, three machine learning algorithms, primarily Decision Trees, XGBoost, and AdaBoost, are utilised to undertake sentiment analysis on products. It was noticed that XGBoost produced a greater detection accuracy of 84.45%.