What question did this study set out to answer?

The study aims to develop a framework for classifying sentiment in multilingual social media content.

April 25, 2026Open Access

The Challenge of Code-Switching: Sentiment Analysis in Multilingual Social Media Data

Key Points

The study aims to develop a framework for classifying sentiment in multilingual social media content.
Designing a pipeline for polarity classification using supervised machine learning techniques.
Utilizing Sentiment140 and Hinglish_Sentiment datasets for training and testing.
Implementing novel preprocessing and cleaning methods tailored for social media content.
Achieved high accuracy in sentiment detection, indicating effective automated analysis.
Demonstrated scalability of the framework for real-time applications like market research and public policy tracking.

Abstract

This an important study as it tackles the issue of parsing huge amounts of informal, unstructured user-generated content on X and Instagram. The proposed work designs a solid pipeline for polarity classification by using supervised machine learning framework using Sentiment140 and HinglishSentiment datasets. The project includes novel preprocessing and cleaning methods for social media-specific, shorthand slangs, and text that is limited by a certain number of characters. Early results show that sentiment detection can be done with a high degree of accuracy, confirmed our belief that this type of automated analysis is precise and scalable; an essential component in real-time market research, public policy tracking and helping clients navigate community mood.

The Challenge of Code-Switching: Sentiment Analysis in Multilingual Social Media Data

Key Points

Abstract

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