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Author: search.filters.author.Kongthong, Chanwit

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    Item type:Publication,
    Unleashing Hidden Business Insights: Harnessing Unstructured Big Data through Text Analysis, NLP, and Visualizations for Budgetary Decisions in Governmental Organizations
    (2024-01-01)
    Kongthong, Chanwit
    ;
    Jitkajornwanich, Kulsawasd
    ;
    Intakosum, Sarun
    Processing Thai language texts can be a challenge due to the complexities of the language, particularly texts from social media and online platforms. This paper introduces an analysis and visualization framework specifically designed to tackle the intricacies associated with processing the Thai language data within the context of online textual content, by utilizing natural language processing (NLP) and visualization techniques. The objectives of this study were to develop an effective Thai text data analysis and visualization framework that allows us to effectively and automatically get a better understanding of the content embedded in Thai textual data. The methodology initiated with a review of existing analysis frameworks and visualization techniques with a specific focus on Thai. The data collection phase encompassed a diverse corpus of Thai text data gathered from online sources. The selected data underwent preprocessing to address language-specific challenges. The proposed Thai analysis and visualization framework consists of multiple stages. Each stage is tailored to accommodate the intricacies of the Thai language, facilitating improved information extraction and text comprehension. The proposed visualization techniques utilize interactive graphs, such as bar charts, line charts, pie charts and donut charts, to offer intuitive and insightful representations of the processed data. Results from our case study show the effectiveness of our Thai analysis framework and visualization techniques in capturing crucial information from online contents written in Thai from governmental organizations.
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    Utilizing Twitter Data for Early Flood Warning in Thailand
    (2018-07-02)
    Jitkajornwanich, Kulsawasd
    ;
    Kongthong, Chanwit
    ;
    Khongsoontornjaroen, Nattaya
    ;
    Kaiyasuan, Jeedapa
    ;
    Lawawirojwong, Siam
    Natural disasters cause significant damage to the country as well as its citizens as we have seen in the news. Drought, wild fire, earthquake and flooding are some examples of the primary natural disasters occurred in Thailand. In this research, we focus on »flooding» and use data from Twitter, where users' mobile devices are utilized as IoT input channels. The goal of this work is to analyze near real-time data (tweets) for early flood warning. Traditional methods in processing, analyzing and reporting a flooding event take quite some time. In social medias (through cellphones), on the other hand, by harvesting crowdsources, potential flooding can be predicted faster - though with the price of reliability of the retrieved tweets. In our research, several techniques are incorporated in order to maximize the accuracy of results, including, tokenization, geo-encoding and decoding, NLP via string matching (Levenshtein's algorithms), and Google APIs for visualization. Finally, the dynamic yet user-friendly map is produced with respect to the posted relevant tweets along their associated frequencies.