{"title":"Emotion Detection System for Malayalam Text using Deep Learning and Transformers","authors":"Anuja K, P. C. Reghu Raj, Remesh Babu K R","doi":"10.1145/3663475","DOIUrl":null,"url":null,"abstract":"<p>Recent advances in Natural Language Processing (NLP) have improved the performance of the systems that perform tasks, such as Emotion Detection (ED), Information Retrieval, Translation, etc., in resource-rich languages like English and Chinese. But similar advancements have not been made in Malayalam due to the dearth of annotated datasets. Because of its rich morphology, free word order and agglutinative character, data preparation in Malayalam is highly challenging. In this paper, we employ traditional Machine Learning (ML) techniques such as support vector machines (SVM) and multilayer perceptrons (MLP), and recent deep learning methods such as Recurrent Neural Networks (RNN) and advanced transformer-based methodologies to train an emotion detection system. This work stands out since all the previous attempts to extract emotions from Malayalam text have relied on lexicons, which are inappropriate for handling large amounts of data. By tweaking the hyperparameters, we enhanced the transformer-based model known as MuRIL to obtain an accuracy of 79%, which is then compared with the only state-of-the-art (SOTA) model. We found that the proposed techniques surpass the SOTA system available for detecting emotions in Malayalam reported so far.</p>","PeriodicalId":54312,"journal":{"name":"ACM Transactions on Asian and Low-Resource Language Information Processing","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Asian and Low-Resource Language Information Processing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3663475","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Recent advances in Natural Language Processing (NLP) have improved the performance of the systems that perform tasks, such as Emotion Detection (ED), Information Retrieval, Translation, etc., in resource-rich languages like English and Chinese. But similar advancements have not been made in Malayalam due to the dearth of annotated datasets. Because of its rich morphology, free word order and agglutinative character, data preparation in Malayalam is highly challenging. In this paper, we employ traditional Machine Learning (ML) techniques such as support vector machines (SVM) and multilayer perceptrons (MLP), and recent deep learning methods such as Recurrent Neural Networks (RNN) and advanced transformer-based methodologies to train an emotion detection system. This work stands out since all the previous attempts to extract emotions from Malayalam text have relied on lexicons, which are inappropriate for handling large amounts of data. By tweaking the hyperparameters, we enhanced the transformer-based model known as MuRIL to obtain an accuracy of 79%, which is then compared with the only state-of-the-art (SOTA) model. We found that the proposed techniques surpass the SOTA system available for detecting emotions in Malayalam reported so far.
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.