Exploring the Correlation between Emojis and Mood Expression in Thai Twitter Discourse

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 : 2024-07-24 DOI:10.1145/3680543

Attapol T. Rutherford, Pawitsapak Akarajaradwong

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Abstract

Mood, a long-lasting affective state detached from specific stimuli, plays an important role in behavior. Although sentiment analysis and emotion classification have garnered attention, research on mood classification remains in its early stages. This study adopts a two-dimensional structure of affect, comprising ”pleasantness” and ”activation,” to classify mood patterns. Emojis, graphic symbols representing emotions and concepts, are widely used in computer-mediated communication. Unlike previous studies that consider emojis as direct labels for emotion or sentiment, this work uses a pre-trained large language model which integrates both text and emojis to develop a mood classification model. Our contributions are three-fold. First, we annotate 10,000 Thai tweets with mood to train the models and release the dataset to the public. Second, we show that emojis contribute to determining mood to a lesser extent than text, far from mapping directly to mood. Third, through the application of the trained model, we observe the correlation of moods during the Thai political turmoil of 2019-2020 on Thai Twitter and find a significant correlation. These moods closely reflect the news events and reveal one side of Thai public opinion during the turmoil.

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探索泰语 Twitter 话语中表情符号与情绪表达之间的相关性

情绪是一种脱离特定刺激的持久情感状态，在行为中扮演着重要角色。虽然情感分析和情绪分类已引起人们的关注，但情绪分类研究仍处于早期阶段。本研究采用由 "愉快度 "和 "激活度 "组成的二维情感结构对情绪模式进行分类。表情符号是代表情绪和概念的图形符号，在以计算机为媒介的交流中被广泛使用。与以往将表情符号作为情绪或情感的直接标签的研究不同，这项工作使用了一个预先训练好的大型语言模型，该模型将文本和表情符号整合在一起，从而开发出一种情绪分类模型。我们的贡献有三方面。首先，我们对 10,000 条泰国推文进行了情绪注释以训练模型，并向公众发布了数据集。其次，我们证明了表情符号在确定情绪方面的作用小于文本，远没有直接映射到情绪上。第三，通过应用训练有素的模型，我们观察了泰国推特上 2019-2020 年泰国政治动荡期间的情绪相关性，并发现了显著的相关性。这些情绪密切反映了新闻事件，揭示了动荡期间泰国舆论的一个侧面。

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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.