| Summary: | Social media platforms such as Facebook and Twitter have enormous amounts of data that can be extracted and analyzed for various purposes. Stock market prediction is one of them. Previous research has shown that there is a correlation between Twitter sentiment – the proportion of positive, negative and neutral tweets – and the changes in companies’ stock prices. The present study investigates if categorizing tweets into a bigger number of categories – anger, disgust, joy, surprise, none - results in stronger correlations being found. In total, 5985 tweets in English about American Airlines, American Express, AstraZeneca and ExxonMobil were extracted and analyzed with the help of sentiment and emotion classifiers trained. Tweet sentiment showed stronger correlations with stock returns than emotion did, although the type of correlation found differed between the companies considered. It is suggested that dividing tweets into fewer categories results in semantically more distinct labels that are easier to distinguish between and that therefore show stronger correlations. Furthermore, the results indicate that the pairs of values showing the strongest correlations depend on the characteristics of each individual company.
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