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Chanakot, Benjamin
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Electrical & Electronics Engineering

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Classifying thai news headlines using an artificial neural network Chanakot, Benjamin; Sanrach, Charun
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4228

Abstract

This research aimed to measure the effectiveness of Thai news headlines classification using an artificial neural network (ANN). The headlines consisted of i) political news, ii) sports news, iii) economic news, and iv) crime news, 1,200 headlines in total. The distribution of headlines was measured by using chi-square, information gain, and term frequency inverse class frequency (TFICF). Threshold default value was set in relation to terms of headlines before cross-validation was employed to categorize the data to examine the efficiency of the model using a neural network algorithm in classifying the headlines. The investigation of the news headline classification efficiency revealed that the 15-fold data division using TFICF was the most accurate in classifying headlines, with the accuracy rate of 99.60% and F-measure rate of 99.05%. Moreover, it was found that when more news headlines were provided as the learning data, the news headline classification became more accurate. Likewise, appropriate threshold value determination facilitated the selection of appropriate features in the headlines and resulted in more effective and accurate classification. Hence, it can be concluded that headline classification will be more accurate if the appropriate amount of learning data exists, and appropriate threshold value was set.
A Thai-language chatbot analyzing mosquito-borne diseases using Jaccard similarity Chanakot, Benjamin; Sanrach, Charun
Bulletin of Electrical Engineering and Informatics Vol 13, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i1.6048

Abstract

The objective of this study is to create a Thai-language chatbot analyzing mosquito-borne diseases using Jaccard similarity with an aim to develop an artificial intelligence (AI)-based chatbot used to analyze Aedes-borne diseases through natural language processing. The analysis occurred when the symptoms provided by users on the chatbot were assessed to select relevant words as text attributes using the term frequency-inverse document frequency (TF-IDF) before the Jaccard similarity was used to measure the similarity of the information on the mosquito-borne disease database. The Line Messaging API was applied to facilitate communication between users and the chatbot through the Line application. The chatbot applied PHP 7.2.34 and MySQL 5.7.32 for database management, with Apache 2.2.29 serving as the bot server. The performance evaluation of the chatbot revealed that the chatbot accurately understood user intentions with an intent accuracy of 85.00%. Likewise, the usability of the chatbot was assessed using the system usability scale (SUS), and it received a score of 89.75, indicating a high level of user-friendliness. Furthermore, it has been found that appropriate tokenization enables accurate feature selection. This leads to improved accuracy in measuring Jaccard similarity. Consequently, the chatbot is capable of providing precise responses that align with the user's intent.
Co-Authors Sanrach, Charun