Detection of Emotions in Hindi-English Code Mixed Text Data

TL;DR

This paper presents a method for detecting emotions in Hindi-English code-mixed text data using advanced NLP models, addressing the challenge of recognizing emotions in informal, mixed-language social media communication.

Contribution

It introduces a dataset of Hindi-English code-mixed sentences annotated for emotions and evaluates multiple NLP models for emotion classification in this context.

Findings

Models achieved high accuracy in emotion detection

Code-mixed data poses unique challenges for NLP

State-of-the-art models outperform traditional methods

Abstract

In recent times, we have seen an increased use of text chat for communication on social networks and smartphones. This particularly involves the use of Hindi-English code-mixed text which contains words which are not recognized in English vocabulary. We have worked on detecting emotions in these mixed data and classify the sentences in human emotions which are angry, fear, happy or sad. We have used state of the art natural language processing models and compared their performance on the dataset comprising sentences in this mixed data. The dataset was collected and annotated from sources and then used to train the models.