A Framework for Mining Thai Public Opinions

Deerosejanadej, C.; Prom-on, Santitham; Achalakul, Tiranee

doi:10.1016/b978-0-12-805394-2.00014-3

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…This method can only be used on social media platforms that have developed API to connect and collect data. Examples include Twitter, Facebook, and YouTube (Sornlertlamvanich et al, 2015;Sarakit et al, 2015;Deerosejanadej et al, 2016;Jotikabukkana et al, 2016;Klaithin & Haruechaiyasak, 2016;Songram et al, 2016;Katchapakirin et al, 2018;Sirihattasak, 2019).…”

Section: Methods Of Data Collectionmentioning

confidence: 99%

“…F-Measure values were 93% and 94%, respectively. In addition, research on sentiment analysis and classification was performed by Chumwatana (2015), Claypo and Jaiyen (2015), Deerosejanadej et al (2016), andHaruechaiyasak et al (2018). Trakultaweekoon and Klaithin (2016) classified texts by sentiment toward a product, service, person, place, or event.…”

Section: Topic Classificationmentioning

confidence: 99%

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A Survey of Automatic Text Classification Based on Thai Social Media Data

Tanantong

Parnkow

2022

International Journal of Knowledge and Systems Science

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In the digital age, the information on social media, such as Facebook, Twitter, and Instagram, is increasing rapidly. Therefore, it has led to studies and researches on social media analytics to extract useful models or knowledge from the data. One of the most interesting topics in social media analytics is text classification on social media data. However, since social media data has a diverse and complex data structure, text analysis and classification are considered a challenging issue that requires a specific technique to implement. The objective of this review paper is to collect and review research related to the automatic classification of Thai text on social media by presenting and explaining the process of text classification on various issues. These include data collection and data sources, amount of data and data preparation for research, feature extraction methods, text classification automated modeling methods, efficacy evaluation and measurement methods, the results of text classification, and summary of the overall trend of research on the topic.

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