基于低资源模型的词汇特征情感分析

Mercy Lalthangmawii , Thoudam Doren Singh
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引用次数: 0

摘要

情感分析是自然语言处理(NLP)的一个重要领域,用于解释用户生成内容中的情感。尽管在广泛使用的语言方面取得了重大进展,但诸如米佐语等资源匮乏的语言仍未得到充分开发。本研究通过开发首个针对米佐语的综合情感分析框架来解决这一差距。我们创建了一个精心标注的数据集,捕捉积极、消极和中性的情绪。使用经典的机器学习模型增强了词汇特征和XLM-RoBERTa迁移学习,我们证明了在低资源环境下情感分析的可行性。我们的方法在Logistic回归中实现了82%的准确率,在XLM-RoBERTa中实现了78%的准确率,这为Mizo情绪分析的未来研究奠定了基准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sentiment analysis of Mizo using lexical features in low resource based models
Sentiment analysis is a vital area of natural language processing (NLP) for interpreting emotions in user-generated content. Although significant progress has been made for widely spoken languages, low-resource languages such as Mizo remain underexplored. This study addresses this gap by developing the first comprehensive sentiment analysis framework for Mizo language. We created a meticulously annotated data set that captures positive, negative, and neutral sentiments. Using classical machine learning models enhanced with lexicon features and transfer learning with XLM-RoBERTa, we demonstrate the feasibility of sentiment analysis in low-resource settings. Our approach achieves an accuracy of 82% with Logistic Regression and 78% with XLM-RoBERTa, which establishes a benchmark for future research in Mizo sentiment analysis.
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