Thai stock news classification based on price changes and sentiments

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dc.contributor.authorNetisopakul, Ponrudee
dc.contributor.authorSaewong, Woranun
dc.date.accessioned2026-08-06T10:36:04Z
dc.date.available2026-08-06T10:36:04Z
dc.date.issued2022-01-01
dc.description.abstractThis 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%.
dc.identifier.citationInternational Journal of Electronic Finance, 11(1), 49-66, 2022
dc.identifier.doi10.1504/IJEF.2022.120360
dc.identifier.issn17460069
dc.identifier.other2-s2.0-85123800939
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/12963
dc.sourceInternational Journal of Electronic Finance
dc.subjectClustering
dc.subjectMachine learning
dc.subjectNatural language processing
dc.subjectSentiment analysis
dc.subjectSET
dc.subjectStock exchange of Thailand
dc.subjectStock news
dc.subjectStock prediction
dc.subjectText classification
dc.subjectThai language processing
dc.titleThai stock news classification based on price changes and sentiments
dc.typeArticle

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