NepEMO: A Multi-Label Emotion and Sentiment Analysis on Nepali Reddit with Linguistic Insights and Temporal Trends

TL;DR

This paper introduces NepEMO, a comprehensive dataset for multi-label emotion and sentiment analysis on Nepali Reddit posts, providing linguistic insights and evaluating various models, with transformers showing superior performance.

Contribution

The work presents the first large-scale, multi-lingual Nepali Reddit dataset with detailed linguistic analysis and model benchmarking for emotion and sentiment classification.

Findings

Transformer models outperform traditional ML and DL models.

Linguistic analysis reveals emotion co-occurrence and sentiment-specific keywords.

The dataset covers posts from 2019 to 2025 in multiple scripts.

Abstract

Social media (SM) platforms (e.g. Facebook, Twitter, and Reddit) are increasingly leveraged to share opinions and emotions, specifically during challenging events, such as natural disasters, pandemics, and political elections, and joyful occasions like festivals and celebrations. Among the SM platforms, Reddit provides a unique space for its users to anonymously express their experiences and thoughts on sensitive issues such as health and daily life. In this work, we present a novel dataset, called NepEMO, for multi-label emotion (MLE) and sentiment classification (SC) on the Nepali subreddit post. We curate and build a manually annotated dataset of 4,462 posts (January 2019- June 2025) written in English, Romanised Nepali and Devanagari script for five emotions (fear, anger, sadness, joy, and depression) and three sentiment classes (positive, negative, and neutral). We perform a detailed analysis of posts to capture linguistic insights, including emotion trends, co-occurrence of emotions, sentiment-specific n-grams, and topic modelling using Latent Dirichlet Allocation and TF-IDF keyword extraction. Finally, we compare various traditional machine learning (ML), deep learning (DL), and transformer models for MLE and SC tasks. The result shows that transformer models consistently outperform the ML and DL models for both tasks.