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Comment Usefulness Classification on Youtube using Artificial Neural Networks
Author(s)
Takhom, Akkharawoot
Chirawat, Pimmada
Boonkwan, Prachya
Date Issued
January 1, 2023
Type
Conference Paper
Abstract
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.
Citation
18th International Conference on Artificial Intelligence and Natural Language Processing and International Conference on Artificial Intelligence and Internet of Things Isai Nlp 2023, 2023