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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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    Improved Identification of Imbalanced Multiple Annotation Intent Labels with a Hybrid BLSTM and CNN Model and Hybrid Loss Function
    (2021-01-01)
    Vatathanavaro, Supawit
    ;
    Pasupa, Kitsuchart
    ;
    Sirirattanajakarin, Sorratat
    ;
    Suntisrivaraporn, Boontawee
    Payment or fund transfer transactions can be annotated by users when they are made through a mobile banking app, for example, SCB Easy app—a mobile banking app by Siam Commercial Bank—allows users to annotate transactions with 40 character texts. The AI<sup>2</sup> framework was used to identify user intentions with the transactions, so that the bank can offer the right product to the right customer at the right time. The framework employed Long Short-Term Memory (LSTM). Commonly, one annotated sample can be interpreted as representing multiple intents, thus we had a multiple label classification problem. However, the original model did not consider the class imbalance, that caused the model to bias toward the majority class. We introduced a new hybrid Bidirectional LSTM and Convolutional Neural Network model in conjunction with a new hybrid loss function to tackle the imbalance. Our model with hybrid loss function performed better than the AI<sup>2</sup> framework with a 4.5% improvement in F<inf>1</inf> -score. Moreover, our hybrid loss function enabled the model to classify minority classes better, when the imbalance ratio became higher, compared with a conventional cross-entropy loss function. In other words, our hybrid loss function made the model to be more efficient in real-world multiple label imbalance problem.
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    Neural network hybrid model of a direct internal reforming solid oxide fuel cell
    (2012-02-01)
    Chaichana, Kattiyapon
    ;
    Patcharavorachot, Yaneeporn
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    Chutichai, Bhawasut
    ;
    Saebea, Dang
    ;
    Assabumrungrat, Suttichai
    A mathematical model is an important tool for analysis and design of fuel cell stacks and systems. In general, the complete description of fuel cells requires an electrochemical model to predict their electrical characteristics, i.e., cell voltage and current density. However, obtaining the electrochemical model is quite a difficult and complicated task as it involves various operational, structural and electrochemical reaction parameters. In this study, a neural network model was first proposed to predict the electrochemical characteristics of solid oxide fuel cell (SOFC). Various NN structures were trained based on the back-propagation feed-forward approach. The results showed that the NN with optimal structure reliably provides a good estimation of fuel cell electrical characteristics. Then, a neural network hybrid model of a direct internal reforming SOFC, combining mass conservation equations with the NN model, was developed to determine the distributions of gaseous components in fuel and air channels of SOFC as well as the performance of the SOFC in terms of power density and fuel cell efficiency. The effects of various key parameters, e.g., temperature, pressure, steam to carbon ratio, degree of pre-reforming, and inlet fuel flow rate on the SOFC performance under steady-state and isothermal conditions were also investigated. A combination of the first principle model and NN presents a significant advantage of predicting the SOFC performance with accuracy and less computational time. © 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.