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Hate Speech Detection in Thai Social Media with Ordinal-Imbalanced Text Classification
(2022-01-01)
Pasupa, Kitsuchart
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Karnbanjob, Werasut
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Aksornsiri, Massakorn
Cyberbullying has become a serious problem in Thai social media. For example, some Thai people posted hate speeches on Myanmar workers in Thailand during the COVID-19 pandemic, which might elevate hate crime. It is imperative and urgent to detect cyberbullying on Thai social media. The task is a text classification problem. Moreover, hate speeches contain the order of severity levels, but many pieces of work did not consider this point in the model. Therefore, we developed a Thai hate-speech classification method with various loss functions to detect such hate speeches accurately. We evaluated them on a corpus of ordinal-imbalanced Thai text. The evaluated outcomes indicated that the best-in terms of $F$1 -score-model was the model with a loss function of a hybrid between an Ordinal regression loss function and Pearson correlation coefficients (common in similarity function). It yielded an average F1-score of 78.38 %-0.88 % significantly higher than the score achieved by a conventional loss function-and an average mean squared error of 0.2478-5.49 % relative improvement. Thus, the proposed hybrid loss function improved the efficiency of the model.
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Particle Size Estimation in Mixed Commercial Waste Images Using Deep Learning
(2021-06-29)
Kittiworapanya, Phongsathorn
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Pasupa, Kitsuchart
;
Auer, Peter
We assessed several state-of-the-art deep learning algorithms and computer vision techniques for estimating the particle size of mixed commercial waste from images. In waste management, the first step is often coarse shredding, using the particle size to set up the shredder machine. The difficulty is separating the waste particles in an image, which can not be performed well. This work focused on estimating size by using the texture from the input image, captured at a fixed height from the camera lens to the ground. We found that EfficientNet achieved the best performance of 0.72 on F1-Score and 75.89% on accuracy.
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Thai Sentiment Analysis via Bidirectional LSTM-CNN Model with Embedding Vectors and Sentic Features
(2018-07-02)
Ayutthaya, Thititorn Seneewong Na
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Pasupa, Kitsuchart
Sentiment analysis is one of the most frequently performed tasks in Natural Language Processing that plays an important role in marketing research. It allows us to understand customer sentiment. The outcomes from this kind of analysis can be used to improve products and services. Recently, a Word2Vec model, a technique for word embedding (converting text into a number) has been developed and used successfully to a degree to get the sentiment of customers from the text responses that they provided. This work attempted to incorporate two more features-part-of-speech and sentic features-to make the analysis more accurate. The part-of-speech feature identifies the type of words that better convey various sentiments, while the sentic feature identifies the emotion underlying certain words. Combining Bidirectional Long Short-term Memory and Convolutional Neural Networks models with several combinations of the features mentioned, we performed a sentiment analysis of Thai children stories and found that the combination of all three features gave the best result at 78.89 % F1-score.

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