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Author: search.filters.author.Boonkwan, Prachya

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    Classifying Lexical Variations in Thai Social Media: A Machine Learning and LLMs Approach
    (2025-01-01)
    Chirawat, Pimmada
    ;
    Leenoi, Dhanon
    ;
    Takhom, Akkharawoot
    ;
    Boonkwan, Prachya
    ;
    Utasri, Tharathon
    Social media platforms like Facebook, X (formerly Twitter), and TikTok produce large amounts of user content, offering valuable data for Social Media Analytics (SMA). However, analyzing Thai social media presents challenges due to lexical variations, such as spelling changes, semantic shifts, and the creation of new words (lexicogenesis). These features, along with the lack of spaces between words in Thai script, complicate traditional text analysis methods. This study addresses these challenges by using a combination of machine learning (ML) models and large language models (LLMs). We employ Bidirectional Long Short-Term Memory (Bi-LSTM) networks, pretrained models like BERT-base-Thai, and advanced LLMs such as LLaMA-3, Mistral, Gemma, and Phi to classify and analyze Thai social media content. Our research enhances the understanding of lexical variations and improves the processing of Thai language in social media, contributing to the development of more effective natural language processing tools.
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    DataDecon: Data Cleansing Tools for Large Language Model with Efficient Decontamination Techniques
    (2024-01-01)
    Yuenyong, Sumeth
    ;
    Buppodom, Norapat
    ;
    Sangkaew, Koravich
    ;
    Boonmeeprakob, Konthee
    ;
    Boonkwan, Prachya
    Large language models (LLMs) play an important role in modern NLP technology as they are versatile for a wide array of NLP tasks. However, constructing an LLM is challenging due to concealed construction pipelines, the lack of cleansed datasets, and hyperparameter settings, making it almost irreproducible. This paper presents an efficient pipeline for constructing an LLM tailored to a low-to-medium-sourced language with a high level of data contamination and tools to cleanse the dataset. Following our pipeline, we constructed OpenThaiGPT, an LLM for Thai, with only open-sourced datasets such as CC100, OSCAR, and mC4, and achieved the state-of-the-art accuracies on our downstream tasks. Here, we disclosed the data statistics and all hyperparameter settings for reproducibility.
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    Comment Usefulness Classification on Youtube using Artificial Neural Networks
    (2023-01-01)
    Takhom, Akkharawoot
    ;
    Chirawat, Pimmada
    ;
    Boonkwan, Prachya
    Social media represents a vast and constantly evolving data resource, utilized across various domains, including business. However, discerning valuable data for business purposes demands significant analysis and labor, leading to potential errors. To address this, we propose employing deep learning models for classifying useful data, focusing on a case study of quality comments on YouTube in the Thai language. Specifically, we experiment with four sequence-To-sequence models: Recurrent Neural (RNN), Long Short-Term Memory (LSTM), bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU). The experimental results reveal that the Bi-LSTM model exhibits the most promising performance, achieving an accuracy of 94%. Furthermore, Bi-LSTM demonstrates remarkable precision of 0.94, recall of 0.95, and an F1-score of 0.95, underscoring its proficiency in the precise classification of quality comments.
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