|
| |
|
|
Web Browser Extension for Page Analysis
Navrátil, Rostislav ; Křivka, Zbyněk (referee) ; Burget, Radek (advisor)
The purpose of this thesis is to create a WebExtensions oriented module for a web browser. An extension module allows the user to submit details about the currently displayed web page and text content to a server application for thorough analysis and displaying the result. It is built on ExtensionAPIs, but is also supported in WebExtensions based web browsers. The communication between the extension module and the server is realized by XMLHttpRequest and the server application itself is implemented in PHP.
|
|
|
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.
|
|
|
Structures and themes of Yusuf Idris' short stories
Provazníková, Adéla ; Ondráš, František (advisor) ; Oliverius, Jaroslav (referee)
This bachelor's thesis deals with the artistic development of the Egyptian writer Yusuf Idris (1927-1991). Based on the study of two short stories collections it describes the changes in his writing from the 1960s to the 1980s. To achieve this goal, the thesis uses the terminology of the theory of fictional worlds developed by Lubomír Doležel, which provides a suitable tool for the characterisation of themes and techniques in Idris' works. Starting from Idris' realistic worlds of the 1950s, the thesis tracks and explains the fundamental change that his writing later underwent. The essential socio-political context is also given. Through detailed textual analysis of selected short stories, the thesis concludes that author's fictional worlds from the 1980s are darker, more self-contained and complicated, despite of some continuity of themes and structural techniques used throughout Idris' work.
|
|
|
Text Analysis in Specialized Translation: Accuracy and Error Rate
Parobková, Alžbeta ; Marcoň, Petr (referee) ; Dohnal, Přemysl (advisor)
Práca sa zameriava na prieskum a aplikáciu metód textovej analýzy, strojového prekladu na vyhodnotenie kvality technických textov, preložených práve pomocou strojového automatického prekladu. Praktická časť využíva tieto metódy na implementáciu algoritmu pre identifikáciu a klasifikáciu chýb. Ďaľšou časťou praktickej časti je aj aplikácia a natrénovanie neurónového modelu pre korekciu týchto chýb. Porovnanie chybovosti a presnosti prekladu rôznymi prekladačmi je potom preukázané nie len kvalitatívne, ale aj kvantitatívne pomocou štandartných metrík.
|
|
|
Reality Show Survivor: Competition, or Conflict?
Blažek, Jakub ; Hájek, Martin (advisor) ; Kolomoiets, Maksym (referee)
This thesis examines competition and conflict in the reality show Survivor. Its goal is to find out to what extent there are competitive and conflicting situations in the show, how they intertwine with each other, and how does this impact the final form of the show. In relation to this, the work also examines to what extent these interactions take place spontaneously, and to what extent they are constructed by the production of the show. It draws on academic literature on competition, conflict, and reality show, in order to backup the research with suitable theory. The research itself is done through the transcription of episodes of the reality show Survivor, which is then subjected to textual and contextual analysis. This enables subsequent interpretation of the findings. This interpretation shows that both competition and conflict have a central role in the show, and at the same time they are greatly interconnected. There is a lot of emphasis on competition in the show, which is especially noticeable during the challenges, where two tribes compete against each other. These challenges are bounded by rules, and they also offer a goal for which both tribes compete. To achieve this goal, it is neccessary to overcome the opponent's tribe. These situations are completely constructed by the production...
|
|
|
FROM TEXT ANALYSIS TO DISCOURSE INTERPRETATION OF DONALD J. TRUMP'S TWEETS
KRATOCHVÍLOVÁ, Nela
Since its creation in 2006, the social platform Twitter has already excelled its function of a medium, originally designated to facilitate sharing experiences, reflections, and ideas. Most operating systems enable access to the Twitter application and this communication platform is being used in both media and marketing fields, for educational purposes, as well as in politics as a means of communicating information and sharing messages considered important enough to be published on the platform. Using Twitter for communication purposes on the political scene is still a fairly new phenomenon and therefore an interesting subject for a linguistic analysis. The diploma thesis uses textual analysis and discourse analysis to describe and to interpret the linguistic strategies employed by the former American President Donald J. Trump in his Tweets. The data - two data sets - subjected to the analysis were collected from the period during the presidential campaign in 2020. The first data set covers the period at the beginning of the campaign, whereas the second data set includes Tweets posted towards the end of the campaign, close to the Election Day. The analysis combines the use of Czech National Corpus tools, namely the application KWords, and a microanalysis of the chosen data sample in order to identify dominant linguistic strategies and to interpret in which way(s) they contribute to the communication strategy of the American President on Twitter. With respect to the field of study, the thesis illustrates the importance of mastering the skills of a textual analysis and discourse interpretation, both prerequisites necessary for a translator, in the proper understanding of the communicative function of a text without which achieving a quality translation into the target language would not be possible. The thesis is written in the English language.
|
|
|
The Early Proses of Růžena Jesenská and its Critical Response.
TRÁVNÍČKOVÁ, Veronika
The bachelor thesis will present story and novel work by Czech writer Růžena Jesenská from 1900-1909 (the files specifically considered are: Novelly, 1909; Touha a láska, 1902; Mimo svět, 1909). In addition to it's own analysis of the relevant texts, we will also monitor the period reception of the works, i.e. the advantages or disadvantages attributed to the works of Jesenská and the literary contexts in which the works were placed at the time of their creation. With the support of period reviews and critics we will try to answer the question, whether the author fulfilled the contemporary idea of artistic or rather popular prose or whether there was a fusion of these two literary models.
|
|
| |
|
|
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.
|
guest ::
