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Author: search.filters.author.Seneewong Na Ayutthaya, ThititornSubject: search.filters.subject.Thai sentiment

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    Hybrid Deep Learning Models for Thai Sentiment Analysis
    (2022-01-01)
    Pasupa, Kitsuchart
    ;
    Seneewong Na Ayutthaya, Thititorn
    Many people use social media in their daily life for entertainment, business, personal communication, and catching up with friends. In social media marketing, sentiment analysis is one of the most popular research topics because it can be employed to perform brand or market research monitoring and to keep an eye on the competitors. Machine learning algorithms have been utilized to carry out the task. In addition, sentiment analysis is essential in cognitive computing. Currently, there are still a limited number of Thai sentiment analysis research. This paper proposes a framework for sentiment analysis in Thai along with Thai-SenticNet5 corpus. The framework employs different types of features, namely, word embedding, part-of-speech, sentic features, and all combinations of these features. Furthermore, we fused deep learning algorithms—convolutional neural network (CNN) and bidirectional long short-term memory (BLSTM)—in different ways and compare it to several other fused combinations. Three datasets in Thai were used in this work: ThaiTales, ThaiEconTwitter, and Wisesight datasets. The experimental results show that combining all three features and fusing deep learning algorithms were able to improve overall performance. The best hybrid deep learning was BLSTM-CNN that achieved F<inf>1</inf>-scores of 0.7436, 0.7707, and 0.5521, on ThaiTales, ThaiEconTwitter, and Wisesight datasets, respectively. According to the experimental results, we conclude that feature combination and hybrid deep learning algorithms can improve the overall performances.
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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.