Sentiment Analysis and The Industrial Growth

Abstract

It is characterized as the method of data mining, view, study, also sentence to predict the emotion of the phrase through the natural language Processing (NLP). The sentiment analysis requires the division of the text into "+ve", "-ve" or "Neutral" three stages. It examines the details and marks the 'better' and 'worse' emotions as good and bad. Thus, the World Wide Web (WWW) has been a major repository of personalized or user-generated raw data in recent years. Using social media, e-commerce platform, operators deliver their opinions, emotions in a comfortable way through movie reviews such as Facebook, Twitter, Amazon, Flipkart etc. In WWW, where, in their everyday interaction, millions of people share their thoughts, either on social media or in e-commence, which may be their emotions and perceptions about things. Such increasing raw data is an incredibly high source of information, either positive or negative, for any decision-making process. The science of emotion analysis has tended to process such enormous data automatically. The primary objective of SA is to define and characterize the data's polarity on the Network. Sentiment analysis is text-based analysis, but the exact polarity of the sentence is challenging to find. This notes that the best solution to achieve even better outcomes must be sought than the previous method or methodology used to find sentence polarity. Therefore, there is a need for advanced data collection tools to find the consumer or customer's polarity or sentiment. A detailed survey of numerous methods is used in this paper in the analysis of emotion, and a novel methodology suggested in this paper.