Abusive Comment Detection in Tamil Code-Mixed Data by Adjusting Class Weights and Refining Features

IF 1.8 4区计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

ACM Transactions on Asian and Low-Resource Language Information Processing Pub Date : 2024-05-18 DOI:10.1145/3664619

Gayathri G L, Krithika Swaminathan, Divyasri Krishnakumar, Thenmozhi D, Bharathi B

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引用次数: 0

Abstract

In recent years, a significant portion of the content on various platforms on the internet has been found to be offensive or abusive. Abusive comment detection can go a long way in preventing internet users from facing the adverse effects of coming in contact with abusive language. This problem is particularly challenging when the comments are found in low-resource languages like Tamil or Tamil-English code-mixed text. So far, there has not been any substantial work on abusive comment detection using imbalanced datasets. Furthermore, significant work has not been performed, especially for Tamil code-mixed data, that involves analysing the dataset for classification and accordingly creating a custom vocabulary for preprocessing. This paper proposes a novel approach to classify abusive comments from an imbalanced dataset using a customised training vocabulary and a combination of statistical feature selection with language-agnostic feature selection while making use of explainable AI for feature refinement. Our model achieved an accuracy of 74% and a macro F1-score of 0.46.

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通过调整类权重和完善特征在泰米尔语代码混合数据中检测辱骂性评论

近年来，互联网各种平台上的内容有很大一部分被发现具有攻击性或辱骂性。辱骂性评论检测可以有效防止互联网用户因接触辱骂性语言而受到不良影响。当评论使用泰米尔语或泰米尔语-英语混合代码文本等低资源语言时，这一问题尤其具有挑战性。迄今为止，还没有任何关于使用不平衡数据集检测辱骂性评论的实质性工作。此外，特别是针对泰米尔语混合代码数据，还没有开展过涉及数据集分类分析和相应创建自定义词汇进行预处理的重要工作。本文提出了一种从不平衡性数据集中对辱骂性评论进行分类的新方法，该方法使用定制的训练词汇，并将统计特征选择与语言无关特征选择相结合，同时利用可解释人工智能进行特征提纯。我们的模型达到了 74% 的准确率和 0.46 的宏观 F1 分数。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACM Transactions on Asian and Low-Resource Language Information Processing Computer Science-General Computer Science

CiteScore

3.60

自引率

15.00%

发文量

241

期刊介绍： The ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP) publishes high quality original archival papers and technical notes in the areas of computation and processing of information in Asian languages, low-resource languages of Africa, Australasia, Oceania and the Americas, as well as related disciplines. The subject areas covered by TALLIP include, but are not limited to: -Computational Linguistics: including computational phonology, computational morphology, computational syntax (e.g. parsing), computational semantics, computational pragmatics, etc. -Linguistic Resources: including computational lexicography, terminology, electronic dictionaries, cross-lingual dictionaries, electronic thesauri, etc. -Hardware and software algorithms and tools for Asian or low-resource language processing, e.g., handwritten character recognition. -Information Understanding: including text understanding, speech understanding, character recognition, discourse processing, dialogue systems, etc. -Machine Translation involving Asian or low-resource languages. -Information Retrieval: including natural language processing (NLP) for concept-based indexing, natural language query interfaces, semantic relevance judgments, etc. -Information Extraction and Filtering: including automatic abstraction, user profiling, etc. -Speech processing: including text-to-speech synthesis and automatic speech recognition. -Multimedia Asian Information Processing: including speech, image, video, image/text translation, etc. -Cross-lingual information processing involving Asian or low-resource languages. -Papers that deal in theory, systems design, evaluation and applications in the aforesaid subjects are appropriate for TALLIP. Emphasis will be placed on the originality and the practical significance of the reported research.