Sentiments in Mixed-Indic Social Media Text a Comprehensive Aspect-Based Analysis

Abstract

Sentiments in Mixed-Indic languages present unique challenges for sentiment analysis due to their multilingual and diverse nature. In this research, we propose a comprehensive aspect-based analysis approach to decipher sentiments in Mixed-Indic social media text. Our method employs advanced natural language processing techniques to identify and evaluate different aspects within the text, allowing for a nuanced understanding of the underlying sentiments. Through experiments on a large dataset of Mixed-Indic social media content, we demonstrate the effectiveness and applicability of our approach. The results reveal valuable insights into the sentiments prevalent in these texts, providing a deeper understanding of the emotions and opinions expressed by users in multilingual social media environments.