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    Ex-ThaiHate: A Generative Multi-task Framework for Sentiment and Emotion Aware Hate Speech Detection with Explanation in Thai
    (2023-01-01)
    Maity, Krishanu
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    Bhattacharya, Shaubhik
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    Phosit, Salisa
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    Kongsamlit, Sawarod
    ;
    Saha, Sriparna
    Social media platforms have both positive and negative impacts on users in diverse societies. One of the adverse effects of social media platforms is the usage of hate and offensive language, which not only fosters prejudice but also harms the vulnerable. Additionally, a person’s sentiment and emotional state heavily influence the intended content of any social media post. Despite extensive research being conducted to detect online hate speech in English, there is a lack of similar studies on low-resource languages such as Thai. The recent enactment of laws like the “right to explanations” in the General Data Protection Regulation has stimulated the development of interpretable models rather than solely focusing on performance. Motivated by this, we created the first benchmark hate speech corpus, called Ex-ThaiHate, in the Thai language. Each post is annotated with four labels, namely hate, sentiment, emotion, and rationales (explainability), which specify the phrases that are responsible for annotating the post as hate. In order to investigate the effect of sentiment and emotional information on detecting hate speech posts, we propose a unified generative framework called GenX, which redefines this multi-task problem as a text-to-text generation task to simultaneously solve four tasks: hate-speech identification, rationale detection, sentiment, and emotion detection. Our extensive experiments demonstrate that GenX significantly outperforms all baselines and state-of-the-art models, thereby highlighting its effectiveness in detecting hate speech and identifying the rationales in low-resource languages. The code and dataset are available at https://github.com/dsmlr/Ex-ThaiHate. Disclaimer: The article contains offensive text and profanity. This is due to the nature of the work and does not reflect any opinion or stance of the authors.
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    Emotion-based music player
    (2019-07-01)
    Chankuptarat, Krittrin
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    Sriwatanaworachai, Raphatsak
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    Chotipant, Supannada
    Nowadays, people tend to increasingly have more stress because of the bad economy, high living expenses, etc. Listening to music is a key activity that assists to reduce stress. However, it may be unhelpful if the music does not suit the current emotion of the listener. Moreover, there is no music player which is able to select songs based on the user emotion. To solve this problem, this paper proposes an emotion-based music player, which is able to suggest songs based on the user's emotions; sad, happy, neutral and angry. The application receives either the user's heart rate or facial image from a smart band or mobile camera. It then uses the classification method to identify the user's emotion. This paper presents 2 kinds of the classification method; the heart rate-based and the facial image-based methods. Then, the application returns songs which have the same mood as the user's emotion. The experimental results show that the proposed approach is able to precisely classify the happy emotion because the heart rate range of this emotion is wide.
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    Using image features and eye tracking device to predict human emotions towards abstract images
    (2016-01-01)
    Pasupa, Kitsuchart
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    Chatkamjuncharoen, Panawee
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    Wuttilertdeshar, Chotiros
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    Sugimoto, Masanori
    Nowadays, emotional semantic image retrieval system enables users to access images that they want in a database according to emotional concept. This leads to affective image classification task which recently attracts researchers’ attention. However, different users may experience different emotions depending on where, in the image, they are gazing on. This paper presents an improved prediction method by taking into account the users eye movement as implicit feedback while they are looking at the image. Our experimental results show that using both eye movement information and image feature together to determine users emotion gave more accurate predictions than using image feature alone.