Page 502 - Kaleidoscope Academic Conference Proceedings 2024

S3.3 A Framework for Fine Grained Sentiment Analysis on Code-Mixed Language for Social Media
User Behaviors
Anand V. Tank (Atmiya University, India); Pratik A. Vanjara (M P Shah Commerce College
Surendranagar, India)

Here, we provide a method that discover sentiments from social media platforms, assesses them,
and transforms them into meaningful data: the "Fine Grained Sentiment Analysis for Code-Mixed
Languages" framework (FGSACML). 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 next natural area for branding is 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 and post 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 method's performance is preliminary demonstrated by
analytical data.

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