Review Mate: A Cutting-Edge Model for Analyzing the Sentiment of Online Customer Product Reviews using ML.NET
Abstract
E-commerce has become increasingly important in recent years due to several factors such as convenience, global reach, lower costs, personalization and uninterrupted access. In e-commerce, product reviews by customers can significantly impact purchasing behavior by providing social proof, establishing trust, aiding decision-making, improving search engine optimization, and increasing sales. Conducting an evaluation of the primary impacts of customer reviews on purchasing behavior through automated machine learning techniques has the potential to facilitate the advancement of diverse online business models. In this scope, we come with a new machine-learning model for evaluating customer sentiment based on product reviews. To this aim, a dataset consisting of 1000 positive and 1000 negative customer reviews was created by collecting publicly shared comments from online shopping websites serving in Turkey with a data collection tool developed by our research group. The model development was carried out on ML.NET, an open-source and cross-platform machine learning framework. In order to reach the most efficient model, a total of 36 machine learning models were explored for the solution of the problem within the scope of the experimental study. As a result, the model named Lbfgs Logistic Regression Binary was found to be the most efficient. The related model provided an accuracy rate of 94.76%. An API service called Review Mate has been developed to expand the potential impact of the proposed machine learning model and enable its use in different online business models. According to the findings, the proposed method outperforms the previous approach in terms of classification performance and also provides avenues for the discovery of new product ideas.
Keywords
Sentiment analysis , Machine learning , Classification , ML.NET
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