Expanding Research Horizons for Hinglish Text by Tackling Challenges and Research Gaps

Abstract

Hinglish, a hybrid of Hindi and English, poses unique challenges for sentiment analysis due to its linguistic complexity, lack of structured grammar, and limited availability of annotated datasets. This research investigates and analyses existing methods for Hinglish and English sentiment analysis and explores the complexities of this code-mixed domain. By analyzing research papers, reports, and white papers, the study identifies key obstacles like language detection, intricate grammar, and limited data availability. It also highlights the difficulties of adapting models to new contexts. The research proposes innovative solutions based on these challenges and emphasizes the need for a multi-faceted approach to achieve accurate sentiment analysis in Hinglish. This paves the way for further innovation in code-mixed language sentiment analysis, and at the same time, it can form the base for building large language models for sentiment analysis as well as llm poorly understood Hinglish in current capacities.