Customer Feedback Analysis using Customer Sentiment from Reviews

Sentiment analysis is tapped as a crucial tool for driving insights into diverse domains. Through the rising volume of users’ content on online platforms, is now becoming a dominant tool. This paper precisely goes through the methods, applications, challenges, and latest improvements that SA is entailed with. It devotes itself to the multifarious aspects of SA, exploring different techniques of dealing with sentimental classification, opinion-spam detection, and assessing online reviews. It sheds light on the importance of the method of Aspect-Based Textual Analysis (ABSA) being used for the identification of sentiments directed to definite aspects or features of entities. This document demonstrates an extensive examination of some updated SA techniques such as lexical and then sophisticated machine learning approaches. It spells out the data collection tools, preprocessing, feature extraction, and classification models utilized in primary tasks (SA). Logistic Regression, Naïve Bayes Classifiers, Random Forest Classifier and Feedforward Neural Networks are evaluated on both Bag of Words and TF-IDF vectorized data, the main mission being to show how they are applied in this intricate process. The research finds that the marketers of nearly all industries can benefit in every step of the decision process from product analysis to consumer insight by incorporating SA into their process.