A Framework for Fine Grained Sentiment Analysis on Code-Mixed Language for Social Media User Behaviours

Abstract

Here, we provide a framework that discovers sentiments from social media platforms, assesses, and transforms them into meaningful data. Social media is changing people's attitudes and habits, which in turn is influencing their choices. Attempting to keep an eye on social networking activity is a useful tool for tracking consumer attitude about products and firms and gauging loyalty from consumers. The framework can be the next natural area for branding based on the polarities on the internet and social media. We present a dynamic solution method for sentiment analysis using the classification of interpersonal data sources. To evaluate the caliber of social information services, we also introduce a brand-new quality model. We utilize public comments, posts through social media as an inspiring case study. Specifically, to pinpoint the comments and posts we concentrate on the spatiotemporal characteristics of the attitudes expressed by social media users. On datasets from the real world, experiments are carried out. Our suggested model’s performance is preliminary demonstrated by performance evaluation matrix