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Automatic Real Time sentiment Analysis of Online Shopping Application: Generic Model
  • Mritunjay Kr. Ranjan,
  • Sanjay Kr. Tiwari,
  • Arif Mohammad Sattar
Mritunjay Kr. Ranjan
Sandip University

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Sanjay Kr. Tiwari
Magadh University
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Arif Mohammad Sattar
Magadh University
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Abstract As part of this study, a unique method for automatic real-time analysis of sentiment in product reviews for online shopping applications is provided. The main goal is to create a model that has a high level of accuracy and is able to categorize product reviews with either a positive, negative, or neutral sentiment. To achieve this goal, a combination of natural language processing (NLP) strategies and machine learning algorithms is employed. The first step in the process, known as pre-processing, cleans up the text data by removing any noise and then applies tokenization and stemming techniques to it so that significant features can be extracted. A variety of machine learning models, such as B. Support Vector Machines (SVM), Naive Bayes and Random Forest, are trained and evaluated using an extensive data set of labeled customer reviews of various products. The development of a web application was chosen as the implementation method for the system in order to enable easy integration with the online trading platform. This solution ensures that processing occurs in real-time, allowing for effective analysis and quick response to users. Users can better understand product reviews and make more informed purchasing decisions when sentiment analysis is integrated into the online shopping experience. The proposed approach is a major advance in the development of sentiment analysis for online trading related applications. His ability to conduct real-time sentiment analysis allows him to uncover key insights into customers’ thoughts and preferences. The method provides a viable way for companies to assess consumer satisfaction and sentiment trends by properly classifying product reviews. As a result, companies can ultimately improve the quality of the products and services they provide. Keyword: Online shopping applications, Sentiment analysis, Product reviews, Natural language processing (NLP), support vector machines (SVM), Naive Bayes, Random forest.