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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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    Text Mining Analysis of Comments in Thai Language for Depression from Online Social Networks
    (2021-01-01)
    Chaiwuttisak, Pornpimol
    The objectives of this research were to analyze the relationship of the phrases or words commonly found in the comments from depression hashtag on Twitter using the association rules. The data used in this study were collected from comments in Thai language via depression hashtag on Twitter during 1 January 2019 to 31 January 2019, in total of 1,500 comments. According to the comments in Thai language on social media collected by using Rapidminer Studio 9 software to get the word about depression and used to analyze relationships of words from a text comment to get the format data Association. The frequency of words and phrases in a form of presentation is used to describe the various opinions about the depression that has a presentation on social media. According to the model performance in each of the above methods, it was found that Euclidean Distance provided the best result due to the smallest average distance at all points in each cluster which was equal to 152.504. The association analysis, a total of 30 association rules were obtained, the support of 0.5% and the 80% confidence.