arXiv 2512.228232025-12-28cs.CL

NepEMO：基于尼泊尔Reddit的多标签情绪与情感分析及语言洞察与时间趋势

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

Department of Computer Science and Application（计算机科学与应用系）；Mechi Multiple Campus, Tribhuvan University（特里布文大学梅奇多校园）；School of Computer Science（计算机科学学院）；Queensland University of Technology（昆士兰理工大学）；Department of Electronics and Computer Engineering（电子与计算机工程系）；Advanced College of Engineering and Management, Tribhuvan University（特里布文大学先进工程与管理学院）；School of Engineering and Technology（工程与技术学院）

Sameer Sitoula, Tej Bahadur Shahi, Laxmi Prasad Bhatt, Anisha Pokhrel, Arjun Neupane

备注：: This paper is under consideration in Neural Computing & Applications (Springer) journal. This version may be deleted or updated at any time, depending on the journal's policy upon acceptance

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AI总结：

NepEMO数据集通过多标签情绪和情感分析，结合语言洞察与时间趋势，提升尼泊尔Reddit帖子的情感识别能力。

摘要：

社交媒体（SM）平台（例如Facebook、Twitter和Reddit）越来越多地被用来分享观点和情绪，特别是在挑战性事件，如自然灾害、流行病和政治选举，以及欢乐场合如节日和庆祝活动期间。在这些SM平台中，Reddit为用户提供了独特的空间，使他们可以匿名表达对敏感问题如健康和日常生活经历和想法的看法。在本工作中，我们提出了一个名为NepEMO的新数据集，用于尼泊尔子论坛帖子上的多标签情绪（MLE）和情感分类（SC）。我们整理并构建了一个手动标注的数据集，包含4,462条帖子（2019年1月至2025年6月），这些帖子用英语、罗马化尼泊尔语和天城文书写，涵盖五种情绪（恐惧、愤怒、悲伤、快乐和抑郁）和三种情感类别（积极、消极和中性）。我们对帖子进行了详细分析，以捕捉语言洞察，包括情绪趋势、情绪共现、情感特定的n-gram以及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，我们比较了各种传统机器学习（ML）、深度学习（DL）和变压器模型在MLE和SC任务中的表现。结果表明，变压器模型在两项任务中均优于ML和DL模型。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。