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    Towards Sentic-Aware Multimodal Models for Cyberbullying Detection in Thai Memes
    (2026-01-01)
    Weradechtaweewon, Nattawat
    ;
    Boondamnoen, Mongkol
    ;
    Pasupa, Kitsuchart
    Cyberbullying has become an increasingly urgent issue in online communities. Memes, a popular form of online expression, often blend text and imagery in emotionally charged, sarcastic, or offensive ways–posing unique challenges for automatic harmful content detection. This work explores sentic-aware multimodal models for cyberbullying detection in Thai memes, with a focus on integrating affective commonsense knowledge through SenticNet-based features that emphasize conceptual reasoning and structured emotion representation. To enable this, we propose ThaiSenticNet 7, a resource adapted for the Thai language by translating from SenticNet 7, which supports the generation of sentic features. We investigate three representations–sentic vectors, sentic spectrograms, and sentic mel-spectrograms–and their integration with various sequential models to form sentic embeddings. These embeddings are fused with textual and visual information, extracted via a fine-tuned WangchanBERTa and a Swin Transformer, respectively, forming a unified multimodal pipeline. Experiments on a curated Thai meme dataset show that incorporating sentic features significantly enhances classification performance, with the best configuration–combining all three modalities–achieving an F<inf>1</inf>-score of 0.8044. Notably, the mel-spectrogram transformation proves particularly effective, suggesting that frequency-domain encoding helps capture subtle affective transitions in text-derived emotional signals. Our findings highlight the value of affective knowledge and multimodal modeling in tackling harmful content in memes.
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    Thai sentiment analysis with deep learning techniques: A comparative study based on word embedding, POS-tag, and sentic features
    (2019-10-01)
    Pasupa, Kitsuchart
    ;
    Seneewong Na Ayutthaya, Thititorn
    A smart city connects physical, information technology, social, and business infrastructures together to leverage their collective intelligence. Feedback drives improvements in service, city development, and quality of life in the city. Therefore, sentiment analysis in real-time of opinions expressed in text form by residents in the city is absolutely necessary. Nowadays, machine learning is widely applied to sentiment analysis of decisions in business, especially deep learning. In this experiment, we evaluated and compared the performances of several conventional deep learning models: Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (Bi-LSTM), in sentiment analysis of Thai children tales. In several previous studies, many features have been used in all of the models mentioned, features such as word embedding that helps a model to understand the semantics of each word, POS-tag that helps a model to understand the grammatical function of words, and sentic that helps a model to understand the emotion of words. Some combinations of these features have also been used. The results of this experiment show that the CNN model that used all three features gave the best result of 0.817 F1-score at p < 0.01, which was significantly better than all other models.
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    Thai sentiment resource using Thai wordnet
    (2019-01-01)
    Netisopakul, Ponrudee
    ;
    Thong-Iad, Kanlaya
    Language corpus and resources are essential for text analytics research. For popular language such as English, the resources are publicly available. However, Thai resources are hardly found. This work proposes to construct Thai sentiment resource from English-Thai dictionaries, Thai WordNet and SenticNet. Unlike an existing previous work [1] in which one Thai term is limited to have only one set of sentic values, which is unrealistic. This new methodology proposes that one Thai term can have more than one sentic values based on its synsets. The proposed approach employs synset information from Thai WordNet and hence able to construct many sets of sentic values for Thai terms. The resulting Thai sentiment resource contains 28,780 terms comparing to 16,584 terms from the previous work. Thai terms and their associated synsets can be visually explored via web application.