Abstract
This research constructs a process flow of social media sentiment analysis and explains relationship between comments on social media and stock. One of the popular social media for Stock Exchange of Thailand investors is Pantip.com, a website providing service as a webboard with tagging feature. In this research, all posts tagged by ‘Stock’ on this website were crawled to files. Then comments were scraped and cleaned. For model training, some comments were labelled into three classes; positive, negative and neutral. Sentiment analysis model was constructed by Naive Bayes Classification technique. In evaluation, the model shown that it performed 74% accuracy. This model was utilised to classify comments into sentiments. When all comments were completely classed, sentiment types were counted by date. Finally, correlation matrices were constructed to find relationship between number of sentiments and stock. The research found that number of sentiments from social media relate to ADVANC and CPALL stock volumes. Moreover, the correlation always reaches to the peak on trading day then it gradually declines with the magnitude depending on the day length after trading day.
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