Thai stock news sentiment classification using wordpair features

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dc.contributor.authorChattupan, Apinan
dc.contributor.authorNetisopakul, Ponrudee
dc.date.accessioned2026-08-06T10:10:21Z
dc.date.available2026-08-06T10:10:21Z
dc.date.issued2015-01-01
dc.description.abstractThai stock brokers issue daily stock news for their customers. One broker labels these news with plus, minus and zero sign to indicate the type of recommendation. This paper proposed to classify Thai stock news by extracting important texts from the news. The extracted text is in a form of a 'wordpair'. Three wordpair sets, manual wordpairs extraction (ME), manual wordpairs addition (MA), and automate wordpairs combination (AC), are constructed and compared for their precision, recall and f-measure. Using this broker's news as a training set and unseen stock news from other brokers as a testing set, the experiment shows that all three sets have similar results for the training set but the second and the third set have better classification results in classifying stock news from unseen brokers.
dc.identifier.citation29th Pacific Asia Conference on Language Information and Computation Paclic 2015, 188-195, 2015
dc.identifier.other2-s2.0-84966801113
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/5920
dc.source29th Pacific Asia Conference on Language Information and Computation Paclic 2015
dc.subjectSentiment classification
dc.subjectText classification
dc.subjectThai stock news
dc.subjectWordpair features
dc.titleThai stock news sentiment classification using wordpair features
dc.typeConference Paper

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