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.
Abstract (translated)
社交媒体（SM）平台（如 Facebook、Twitter 和 Reddit）越来越被用于分享观点和情感，尤其是在自然灾害、流行病和政治选举等挑战性事件以及节日和庆典等欢乐时刻。在这些社交媒体平台上，Reddit 为用户提供了匿名表达对健康和日常生活等问题看法的独特空间。在这项工作中，我们介绍了一种名为 NepEMO 的新型数据集，该数据集用于尼泊尔 subreddit 帖子的多标签情绪（MLE）分类和情感（SC）分类。我们整理并构建了一个包含 4,462 条帖子的手动注释数据集（时间跨度为 2019 年 1 月到 2025 年 6 月），这些帖子用英语、罗马化尼泊尔语和梵文书写，涵盖五种情绪（恐惧、愤怒、悲伤、喜悦和抑郁）以及三个情感类别（正面、负面和中立）。我们对帖子进行了详细分析，以捕捉语言学见解，包括情绪趋势、情绪共现、特定情感的 n-gram 和使用潜在狄利克雷分配和 TF-IDF 关键词提取进行的主题建模。最后，我们将多种传统机器学习（ML）、深度学习（DL）以及转换器模型用于 MLE 和 SC 任务，并进行了比较。结果表明，在这两项任务中，转换器模型始终优于 ML 和 DL 模型。
URL
https://arxiv.org/abs/2512.22823
