Quality achhi hai (is good), satisfied! Towards aspect based sentiment analysis in code-mixed language

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Computer Speech and Language Pub Date : 2024-06-12 DOI:10.1016/j.csl.2024.101668

Mamta , Asif Ekbal

{"title":"Quality achhi hai (is good), satisfied! Towards aspect based sentiment analysis in code-mixed language","authors":"Mamta , Asif Ekbal","doi":"10.1016/j.csl.2024.101668","DOIUrl":null,"url":null,"abstract":"<div><p>Social media, e-commerce, and other online platforms have witnessed tremendous growth in multilingual users. This requires addressing the code-mixing phenomenon, i.e. mixing of more than one language for providing a rich native user experience. User reviews and comments may benefit service providers in terms of customer management. Aspect based Sentiment Analysis (ABSA) provides a fine-grained analysis of these reviews by identifying the aspects mentioned and classifies the polarities (i.e., positive, negative, neutral, and conflict). The research in this direction has mainly focused on resource-rich monolingual languages like English, which does not suffice for analyzing multilingual code-mixed reviews. In this paper, we introduce a new task to facilitate the research on code-mixed ABSA. We offer a benchmark setup by creating a code-mixed Hinglish (i.e., mixing of Hindi and English) dataset for ABSA, which is annotated with aspect terms and their sentiment values. To demonstrate the effective usage of the dataset, we develop several deep learning based models for aspect term extraction and sentiment analysis, and establish them as the baselines for further research in this direction. <span><sup>1</sup></span></p></div>","PeriodicalId":50638,"journal":{"name":"Computer Speech and Language","volume":"89 ","pages":"Article 101668"},"PeriodicalIF":3.1000,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0885230824000512/pdfft?md5=d4cf7f510d6f46e21b19e99b8421ebc3&pid=1-s2.0-S0885230824000512-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Speech and Language","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0885230824000512","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}

引用次数: 0

Abstract

Social media, e-commerce, and other online platforms have witnessed tremendous growth in multilingual users. This requires addressing the code-mixing phenomenon, i.e. mixing of more than one language for providing a rich native user experience. User reviews and comments may benefit service providers in terms of customer management. Aspect based Sentiment Analysis (ABSA) provides a fine-grained analysis of these reviews by identifying the aspects mentioned and classifies the polarities (i.e., positive, negative, neutral, and conflict). The research in this direction has mainly focused on resource-rich monolingual languages like English, which does not suffice for analyzing multilingual code-mixed reviews. In this paper, we introduce a new task to facilitate the research on code-mixed ABSA. We offer a benchmark setup by creating a code-mixed Hinglish (i.e., mixing of Hindi and English) dataset for ABSA, which is annotated with aspect terms and their sentiment values. To demonstrate the effective usage of the dataset, we develop several deep learning based models for aspect term extraction and sentiment analysis, and establish them as the baselines for further research in this direction. ¹

查看原文本刊更多论文

质量 achhi hai（很好），满意！在代码混合语言中实现基于方面的情感分析

社交媒体、电子商务和其他在线平台见证了多语言用户的巨大增长。这就需要解决代码混合现象，即混合使用一种以上的语言，以提供丰富的本地用户体验。用户评论和意见可使服务提供商在客户管理方面受益。基于方面的情感分析（ABSA）通过识别所提及的方面并对极性（即正面、负面、中性和冲突）进行分类，对这些评论进行精细分析。该方向的研究主要集中在英语等资源丰富的单语言上，这不足以分析多语言代码混合的评论。在本文中，我们引入了一项新任务，以促进对混合代码 ABSA 的研究。我们为 ABSA 提供了一个基准设置，创建了一个混合编码的 Hinglish（即印地语和英语混合）数据集，该数据集标注了方面术语及其情感值。为了证明该数据集的有效使用，我们开发了几个基于深度学习的方面词提取和情感分析模型，并将它们作为该方向进一步研究的基线。1

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Computer Speech and Language 工程技术-计算机：人工智能

CiteScore

11.30

自引率

4.70%

发文量

审稿时长

22.9 weeks

期刊介绍： Computer Speech & Language publishes reports of original research related to the recognition, understanding, production, coding and mining of speech and language. The speech and language sciences have a long history, but it is only relatively recently that large-scale implementation of and experimentation with complex models of speech and language processing has become feasible. Such research is often carried out somewhat separately by practitioners of artificial intelligence, computer science, electronic engineering, information retrieval, linguistics, phonetics, or psychology.