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PDF Text Extraction
Kubík, Petr ; Otrusina, Lubomír (referee) ; Schmidt, Marek (advisor)
Bachelor's thesis is concerned with text extraction from PDF dokument which contains mainly multi-column text. There's a description of PDF structure and analysis of text extraction from PDF document. Thesis is focused on suggestion of algorithm's implementation of improving text extraction.
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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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PDF Text Extraction
Kubík, Petr ; Otrusina, Lubomír (referee) ; Schmidt, Marek (advisor)
Bachelor's thesis is concerned with text extraction from PDF dokument which contains mainly multi-column text. There's a description of PDF structure and analysis of text extraction from PDF document. Thesis is focused on suggestion of algorithm's implementation of improving text extraction.
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Representation of Text and Its Influence on Categorization
Šabatka, Ondřej ; Chmelař, Petr (referee) ; Bartík, Vladimír (advisor)
The thesis deals with machine processing of textual data. In the theoretical part, issues related to natural language processing are described and different ways of pre-processing and representation of text are also introduced. The thesis also focuses on the usage of N-grams as features for document representation and describes some algorithms used for their extraction. The next part includes an outline of classification methods used. In the practical part, an application for pre-processing and creation of different textual data representations is suggested and implemented. Within the experiments made, the influence of these representations on accuracy of classification algorithms is analysed.
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