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Analysis of stock market sentiment with social media
 Čermák, Vojtěch ; Baruník, Jozef (advisor) ; Vacek, Pavel (referee)
In the thesis, we explored prospects of extracting sentiment contained in Twitter messages. We proposed novel approach consisting of directly predicting the volatility on stock market by features obtained from the text documents using suitable document representation. We compared the performance of standard document vectorisation methods as well as a novel approach based on aggregating word vectors created by word embeddings. We showed that direct modelling of a market variable is possible with most of the proposed vectorisation techniques. In particular, the strong predictive power of aggregated word embeddings suggests that they are excellent sentiment representation, because they are independent of message volume and they capture well the semantical information in the tweets. Besides, our findings suggest that aggregating word embeddings vectorisation is viable approach even for large documents.
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