Product reviews and sentiment analysis using machine learning

Computer Engineering

Natural language processing, opinion mining, text summarization, and other related research fields are hot right now. Sentiment analysis or opinion mining utilizes many NLP techniques and tools to analyze unstructured data in order to obtain people's emotions toward specific entities. Essentially, these strategies allow a system to understand what humans are saying. Sentiment analysis employs many ways to detect the sentiment of a text or sentence [1]. People communicate their subjective opinions and experiences with one another on various online platforms. Obtaining relevant information on a product can be a time-consuming process. Companies may be unaware of all of their customers' needs. Product reviews can be used to determine how people feel about a particular topic. However, because these are lengthy, a summary of positive and negative reviews is required. The main focus of this paper is a study of the methods and techniques used to extract a feature-wise summary of the product and analyze it to generate an accurate review. More product review websites will be included in the future, as well as higher-level natural language processing jobs. Using the best and most innovative strategies or tools. for more accurate results, the system excepts only those keywords in the dataset and eliminates the rest of the terms.

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