Prediction Models for Tourism Stock Market Trends during COVID-19 pandemic using News Sentiment Analysis with Data Mining: A Case Study
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Abstract
This work introduces a process for predicting the trends of stocks in the tourism sector during COVID-19 pandemic using sentiment analysis of COVID19 news headlines with data mining techniques. The COVID-19 news headlines are first collected daily and analyzed via sentiment analysis to obtain their polarity based on naïve Bayes and neural network techniques. These polarity results are then used with the related stock historical data to predict the trend of the stock prices by K-nearest neighbor and decision tree classifications. In our numerical experiments, seven major stocks from the tourism and hotel business operated in Thailand are considered. Our proposed prediction models are shown to have accuracies ranging around 70%- 90%. The highest accuracy of about 90% is achieved when a neuron network is used in the sentiment analysis with the decision tree for predicting the stock trends.
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