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Start date: 2022Subject: search.filters.subject.Sentiment analysis

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Now showing 1 - 4 of 4
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    Transformer-Based Sentiment Classification for Innovative Customer Feedback Analysis in Thai Cosmetic Industry
    (2026-01-01)
    Tantiathimongkhon, Theerawut
    ;
    Limpisiri, Tanintorn
    ;
    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
    ;
    Roonrakwit, Pattarapan
    ;
    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
    ;
    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
    ;
    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%.