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Transformer-Based Sentiment Classification for Innovative Customer Feedback Analysis in Thai Cosmetic Industry
(2026-01-01)
Tantiathimongkhon, Theerawut
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Limpisiri, Tanintorn
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Saengpan, Thanakit
This research aims to develop an effective sentiment analysis system for classifying Thai cosmetic reviews, which present unique linguistic challenges such as the lack of word boundaries, the use of domain-specific terminology, and the prevalence of sarcasm in online discourse. We propose a hybrid approach that combines domain-specific pre-processing with machine learning and deep learning architectures, constructing a specialized beauty corpus and extracting sentiment-bearing keywords. We compare the performance of three models: Support Vector Machine (SVM), Bidirectional LSTM (Bi-LSTM), and fine-tuned WangchanBERTa. The experimental results demonstrate that WangchanBERTa significantly outperforms both traditional approaches, achieving an F1-score of 0.9450, compared to 0.8652 for SVM and 0.7600 for Bi-LSTM. Error analysis of the SVM model reveals specific challenges of the Thai language, such as complex negation usage and temporal sentiment shifts, which are effectively addressed by the Transformer-based architecture. The proposed system offers scalable solutions for both large e-commerce platforms and small enterprises, enabling smart customer feedback analysis that enhances business productivity and supports data-driven resource management. This study showcases the effectiveness of integrating domain-specific natural language processing techniques with pre-trained language models, providing a robust benchmark for sentiment analysis in the beauty industry and contributing to technological innovation and economic growth in Thailand's digital economy.
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Sentiment analysis of the awareness of environmental sustainability
(2024-01-01)
Kularbphettong, Kunyanuth
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Roonrakwit, Pattarapan
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Boonseng, Chongrag
This study examines the sentiment analysis of awareness of environmental sustainability. Environmental sustainability is the responsible management and utilization of Earth's natural resources to meet the needs of the present generation and ensure that future generations will access those resources. The awareness of environmental sustainability has been growing globally as people, businesses, and governments recognize the importance of preserving the planet for current and future generations. Sentiment analysis of environmental sustainability involves evaluating opinions, attitudes, and emotions expressed in texts related to environmental sustainability, and analyzing sentiment can provide insights into public perception, awareness, and engagement with environmental issues. This exploratory study's primary goal is to conduct social media opinion mining in the context of Thai people's environmental sustainability. The paper presented how to build a model of sentiment analysis with linguistic analysis, including data preprocessing steps, feature extraction, and model constructions. The techniques used in this research include Logistic Regression, Random Forests, Support Vector Machine, Word Segmentation and Bag of Words. The result shows that the model is able to categorize sentiment analysis opinions in the sustainability context primarily in positive terms. The positive sentiments suggest a sustained, long-term shift in awareness, or they might be influenced by specific events or trends. However, positive sentiment analysis results are expressed towards environmental sustainability initiatives, such as renewable energy projects, waste reduction efforts, or conservation programs. Moreover, public awareness plays a crucial role in influencing individual behavior, corporate practices, and government policies towards a more sustainable and environmentally conscious future.
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Hybrid Deep Learning Models for Thai Sentiment Analysis
(2022-01-01)
Pasupa, Kitsuchart
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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 stock news classification based on price changes and sentiments
(2022-01-01)
Netisopakul, Ponrudee
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Saewong, Woranun
This research investigates the daily stock news influences toward a company's stock price direction in the Stock Exchange of Thailand. First, machine learning's text classification methods, namely, naïve Bayes, decision tree, random forest, support vector machine, and the three-layer and the five-layer backpropagation neural networks, are applied to predict the stock price directions using stock news collected during the year 2018. Then, the stock news sentiment is incorporated to help improve the prediction accuracy. Last, a meaningful grouping of stock news is carried out to further improve the direction prediction. The testing dataset collected from January to March 2019 stock news are used for model evaluations. The best accuracy obtained from the baseline dataset using stock news only is 78.6%. When dataset is augmented with sentiments and grouped, the best accuracy increases to 90.6%.
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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
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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.
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Cross domain sentiment classification of Thai reviews using co-train model
(2019-01-01)
Boonpetch, Warakorn
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Chitsobhuk, Orachat
Online reviews are significant sources of information, which is useful for supporting customer and entrepreneur decision in terms of product and service satisfaction analysis. Online reviews containing feedback from various domains makes it difficult to analyze and classify all comments at once. The proposed technique analyses the cross-domain Thai review data using a co-train machine learning model. The co-train model consists of multiple single domain specific models followed by refinement analysis for the final sentiment classification. This allows for full flexibility in training of each individual domain, which can lessen the limitation on training complexity due to simple training on single domain. The experiments have been conducted on Wongnai restaurant domain and IMDB movie domain data. Our co-train model can achieve the highest average accuracy of 86.10 percent for cross-domain sentiment classification with approximately 38 seconds processing time.
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Mining social media crowd trends from thai text posts and comments
(2019-01-01)
Thanasopon, Bundit
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Buranapanitkij, Jirawin
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Netisopakul, Ponrudee
Text mining from social media stream has attracted wide interests from both businesses and academics. Very large numbers of self-posts from crowd sources contains hidden trends, which can be valuable to a business enterprise. Crowd trend mining methods, together with an easily understood visual presentation, are thus in great demand. We present an approach to mining crowd trends from Thai text posts. A Thai language preprocessing module was necessary to transform continuous text into series of words. Our method could then mine general unforeseen crowd trends by using an automatic context extraction technique, tf-idf score and an aggregated opinion score calculated from automatically classified sentiments for each post or comment. The best sentiment classifier was chosen based on extensive experiments on the same data source. These scores were combined into one unified term popularity which was visualized as a word cloud on a web application. A case study used a popular Thai discussion website-Pantip. com-and achieved three interwoven desired goals: (1) extraction of general and unforeseen crowd trends from a Thai discussion website, (2) assigning unified popularity scores to each candidate term and (3) presenting those terms to end users in an easily comprehended form.
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Business popularity analysis from twitter
(2018-01-01)
Yaisawas, Pajaree
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Lerdsri, Sukanlaya
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Thanasopon, Bundit
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Netisopakul, Ponrudee
Social media is increasingly utilized for sharing information of online products, from business owners to customers, as well as among customers themselves. In order to utilize these sharing information, this paper proposes and demonstrates the methodology for analyzing business brand popularity based on Twitter posts. The analysis can be visualized by implementing a web application that keeps track and analyzes Twitter posts mentioning about cosmetic and beauty product. Specifically, the application focuses on Twitter posts in Thai; and its key features are, (1) identifying brands being in trend, (2) analyzing and virtualizing statistics provided by Twitter, and (3) classifying Twitter posts’ sentiment into positive, negative and objective. The website provides useful insights to brand owners aiming at exploiting social media and to customers buying products from those brands.
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Hypothesis testing based on observation from Thai sentiment classification
(2017-06-01)
Netisopakul, Ponrudee
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Pasupa, Kitsuchart
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Lertsuksakda, Rathawut
This work focuses on error analyzes from the Support Vector Machine (SVM) classification on Thai children stories at a sentence level. The construction of the Sentiment Term Tagging System (STTS) program allows the researchers to make observations and hypothesize around the areas where most anomalies occur. Three hypotheses, based on terms sentiment chosen for SVM predictions, are evidently proved to hold. In addition, a number of ways to improve the Thai sentiment classification research are suggested, including considerations to add negation into the process, add weighing scheme for different part-of-speech, disambiguate word senses, and update the Thai sentiment resource.
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Sentiment analysis of Thai children stories
(2016-09-01)
Pasupa, Kitsuchart
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Netisopakul, Ponrudee
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Lertsuksakda, Rathawut
Sentiment Text Tagging System (STTS) with Thai sentiment resource has been developed and used to tag emotions directly to words and sentences in Thai children stories. The Thai sentiment resource, developed from SenticNet2 resource, groups emotions into four independent but concomitant dimensions: pleasantness, attention, sensitivity and aptitude. The measure of each dimension is called a sentic value of that dimension. Thai sentiment resource stores each word’s sentic value and polarity value, a value calculated from the sentic value, in the form of floating point number. The resource was constructed from bi-directional translation of 14,244 English terms in SenticNet2 into 16,584 Thai terms. The main purpose of this study was to implement a sentiment analysis of Thai children stories system with support vector machine using a set of proposed discriminating features for classifying emotions. It was found that the system can achieve 75.67 % of accuracy.

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