Abstract

Abstract: Generally, people trust products based on product reviews and ratings. Thoughts can affect an organization or profile of a brand. The corporation has to assess market reactions towards its goods. However, it is not straightforward to track and organize popular reviews. Many public views are hard to process in social media manually. A methodology is then required to categories positive or negative general assessments automatically. Online feedback will provide customers with insight into the product's consistency, efficiency, and advice; this offers prospective buyers a better understanding of the product. One such unrealized opportunity is the usability of web assessments from suppliers to fulfill client requirements by evaluating beneficial feedback. Excellent and negative reviews play a significant role in assessing customer needs and the quicker collection of product input from consumers. Sentiment Analysis is a computer study that extracts contextual data from the text. In this study, a vast number of online mobile telephone ratings are analyzed. We classify the text as positive and negative, but we also included feelings of frustration, expectation, disgust, apprehension, happiness, regret, surprise, and confidence. This delimited grouping of feedback helps to assess the product, allowing buyers to decide better holistically.Keywords— Machine Learning, Social Media, Text Mining, Text Classification, Sentiment Analysis, Online Reviews