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Impact of European Central Bank and Federal Reserve System statements on cryptocurrency markets via sentiment analysis
Krejcar, Vilém ; Krištoufek, Ladislav (advisor) ; Čech, František (referee)
This study explores the impact of public statements from major central banks, specifically the FED and the ECB, on Bitcoin volatility from 2018 to 2021. Utilizing high-frequency data, we computed Bitcoin's volatility and extracted sentiment scores from the central banks' communications using two methods: the FinBERT language model and the state-of-the-art Generative AI GPT-4 model with tailored prompt. The GPT-4 model, capturing more nuanced senti- ment from text, was deemed superior. Our analysis involved comparing various models, with the HAR model emerging as the most e ective for this study. The research findings are particularly significant: negative sentiment from the ECB during the pandemic was associated with immediate and significant increases in Bitcoin volatility, indicating a market reaction of caution when faced with negative emission. These findings highlight the significant impact of central bank sentiment on Bitcoin volatility, confirming the initial hypothesis of this research. Additionally, the results provide a motivation to incorporate Genera- tive Artificial Intelligence into academic research as a tool for uncovering novel insights. JEL Classification C32, C55, C58, E58, G15 Keywords central banks, sentiment analysis, volatility, Bit- coin, GenAI, HAR, FED, ECB Title Impact of European...
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Interviews 2.0 - Using AI for oral historians
Haubert, Marek ; Hlaváček, Jiří (advisor) ; Wohlmuth Markupová, Jana (referee)
This diploma thesis explores the integration of oral history with modern information technologies (IT), especially Artificial Intelligence (AI), aiming to investigate how these technologies can enrich the practice of oral historians and make the processing of oral historical interviews more efficient. It demonstrates, through practical examples, the possibilities of integrating AI and IT services at all stages of oral historical research, from interview preparation to realization, subsequent transcription, analysis, interpretation, and up to its security, archiving, and public publication. The thesis emphasizes practical demonstrations of technology use and research on available services and tools that can facilitate recording interviews, their transcription, sentiment analysis, or metadata creation.
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Analysis of Social Media Content Discussing Czech Mobile Operators
Pavlů, Jan ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
The main topic of this thesis is sentiment analysis of posts obtained from a social networks. The posts are about czech mobile network operators. The essential part of implemented system is also data visualization. The sentiment analysis is done using machine learning techniques. Downloaded posts are cleaned, lemmatized and transformed to feature vectors. Stochastic Gradient Descent algorithm is used for classification. Analyzed data are visualized in charts and as the list of posts. The system provides tools for text categorization. The accuracy, precision, recall and F1 score of sentiment analysis is about 75%. The accuracy of post categorization is high (about 80%), but precision, recall and F1 score are low (about 30%). This is the reason why post categorization isn't automatically done. The benefit of the system it that it automatically collects data from different sources, analysis them and displays them. It also provides tools for manual change of sentiment/categories which can lead to better system characteristics with some help of users.
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Sentiment Analysis with Use of Data Mining
Sychra, Martin ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
The theme of the work is sentiment analysis, especially in terms of informatics (marginally from a linguistic point of view). The linguistic part discusses the term sentiment and language methods for its analysis, e.g. lemmatization, POS tagging, using the list of stopwords etc. More attention is paid to the structure of the sentiment analyzer which is based on some of the machine learning methods (support vector machines, Naive Bayes and maximum entropy classification). On the basis of the theoretical background, a functional analyzer is projected and implemented. The experiments are focused mainly on comparing the classification methods and on the benefits of using the individual preprocessing methods. The success rate of the constructed classifier reaches up to 84 % in the cross-validation.
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Sentiment Analysis of Czech and Slovak Social Networks and Web Discussions
Sojka, Matěj ; Dočekal, Martin (referee) ; Smrž, Pavel (advisor)
Thanks to digitalization, the spread of opinions in the population has accelerated sharply in the recent years, however the need to understand them has not changed. The goal of this thesis was to create a system for automatic data collection from social media and web discussions and sentiment analysis in Czech and Slovak language. The system has a web interface for visualizing results and configuring data analysis. The system is capable of offering topics to the user that it considers to occur in the selected data and group posts based on user-defined opinions.
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Sentiment Analysis of Czech Social Networks and Web Discussions on Retail Chains
Bolješik, Michal ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
The goal of this thesis is to design and implement a system that analyses data from the web mentioning Czech grocery chain stores. Implemented system is able to download such data automatically, perform sentiment analysis of the data, extract locations and chain stores' names from the data and index the data. The system also includes a user interface showing results of the analyses. The first part of the thesis surveys the state of the art in collecting data from web, sentiment analysis and indexing documents. A description of the discussed system's design and its implementation follows. The last part of the thesis evaluates implemented system
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Sentiment Analysis for the Field of Computer Games
Balajka, Pavel ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
The thesis deals with sentiment analysis extracted from opinions of users on social \mbox{networks}. It describes a general system that was created for presented purpose and specialised on the field of strategic computer games. In particular we unravel the problems of acquiring data from social networks, sentiment analysis and results presentation to the user. We mention particular ways of text processing e.g. tokenization and unnecessary word filtration, for purpose of more effective sentiment analysis and we mention machine learning methods e.g. Decision Trees and Naive Bayes, and their usage. Next we describe design of desired system and its implementation with chosen parts and methods. In the end we compare results of tests of sentiment analyzator done under various circumstances.
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Expert System for Decision-Making on Stock Markets Using Investor Sentiment
Janková, Zuzana ; Lenort, Radim (referee) ; Zinecker, Marek (referee) ; Chramcov, Bronislav (referee) ; Dostál, Petr (advisor)
The presented dissertation examines the potential of using the sentiment score extracted from textual data with historical stock index data to improve the performance of stock market prediction through the created model of the expert system. Given the large number of financial-related text documents published by both professional and amateur investors, not only on online social networks that could have an impact on real stock markets, but it is also crucial to analyze and in particular extract financial texts published by different users. investor sentiment. In this work, investor sentiment is obtained from online financial reports and contributions published on the financial social platform StockTwits. Sentiment scores are determined using a hybrid approach combining machine learning models with the teacher and neural networks, with multiple lexicons of positive and negative words used to classify sentiment polarity. The influence of sentiment score on the stock market through causality, cointegration and coherence is analyzed. The dissertation proposes a model of an expert system based on fuzzy logic methods. Fuzzy logic provides remarkable features when working with vague, inaccurate or unclear data and is able to deal with the chaotic environment of stock markets. In recent scientific studies, it has gained in popularity a higher level of fuzzy logic, which is referred to as type-2 fuzzy logic. Unlike the classic type-1 fuzzy logic, this higher type is able to integrate a certain level of uncertainty between the dual membership functions. However, this type of expert system is considerably neglected in the subject issue of stock market prediction using the extracted investor sentiment. For this reason, the dissertation examines the potential to use and the performance of type-2 fuzzy logic. Specifically, several type-2 fuzzy models are created. which are trained on historical stock index data and sentiment scores extracted from text data for the period 2018-2020. The created models are assessed to measure the prediction performance without sentiment and with the integration of investor sentiment. Subsequently, based on the created expert model, the investment strategy is determined, and its profitability is monitored. The prediction performance of fuzzy models is compared with the performance of several comparison models, including SVM, KNN, naive Bayes and others. It has been observed from experiments that fuzzy logic models are able to improve prediction by appropriate setting of membership and uncertainty functions contained in them and are able to compete with classical expert prediction models, which are standardly used in research studies. The created model should serve as a tool to support investment decisions for individual investors.
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Sentiment Analysis from Movie Reviews
Bílý, Daniel ; Jon, Josef (referee) ; Smrž, Pavel (advisor)
This thesis is focused on creating a system which is capable of downloading movie reviews from the web and analysingthem. There is several sources of movie reviews, Czech and English (čsfd, fdb, imdb and rotten tomatoes). The sentiment analysis is performed using machine learning. Results of the analysis are shown in a browser.
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