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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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Non-Supervised Sentiment Analysis
Karabelly, Jozef ; Landini, Federico Nicolás (referee) ; Fajčík, Martin (advisor)
Cieľom tejto práce je odprezentovať prehľad aktuálneho výskumu v oblasti analýzy sentimentu bez priameho učiteľa a identifikovať potenciálne smery výskumu. Okrem toho práca predstavuje novú účelovú funkciu na predtrénovanie, ktorá nevyžaduje priamy supervíziu. Rozšírenie modelu predstavenou účelovou funkciou, pridanie vrstvy neurónovej siete a následné samotné natrénovanie ukazujú sľubné výsledky. Rozšírený model naznačil schopnosť zakódovať abstraktné reprezentácie celkového sentimentu, emócií a sarkazmu. Pre účely použitia predstavenej účelovej funkcie bol nazbieraný vlastný dataset. Na základe experimentov vykonaných s rozšíreným modelom sú odprezentované možné smery výskumu a budúce vylepšenia.
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Text Mining Based on Artificial Intelligence Methods
Povoda, Lukáš ; Tučková,, Jana (referee) ; Brezany, Peter (referee) ; Burget, Radim (advisor)
This work deals with the problem of text mining which is becoming more popular due to exponential growth of the data in electronic form. The work explores contemporary methods and their improvement using optimization methods, as well as the problem of text data understanding in general. The work addresses the problem in three ways: using traditional methods and their optimizations, using Big Data in train phase and abstraction through the minimization of language-dependent parts, and introduction of the new method based on the deep learning which is closer to how human reads and understands text data. The main aim of the dissertation was to propose a method for machine understanding of unstructured text data. The method was experimentally verified by classification of text data on 5 different languages – Czech, English, German, Spanish and Chinese. This demonstrates possible application to different languages families. Validation on the Yelp evaluation database achieve accuracy higher by 0.5% than current methods.
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Processing of User Reviews
Cihlářová, Dita ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
Very often, people buy goods on the Internet that they can not see and try. They therefore rely on reviews of other customers. However, there may be too many reviews for a human to handle them quickly and comfortably. The aim of this work is to offer an application that can recognize in Czech reviews what features of a product are most commented and whether the commentary is positive or negative. The results can save a lot of time for e-shop customers and provide interesting feedback to the manufacturers of the products.
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Estimation of Emotions from a Text
Dufková, Aneta ; Fajčík, Martin (referee) ; Szőke, Igor (advisor)
This thesis describes a process of estimation of emotions from a text using machine learning. The process starts with research of existing methods, continues with choosing a suitable method and experimenting. It uses several datasets, combines them and tests different techniques of text preprocessing. The result is a web interface which uses the pretrained model and allows to estimate emotions from Twitter posts.
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Analýza videozáznamov správ z oblasti finančných trhov
Mikula, Michal
This work deals with the analysis of video recordings of reports from the field of financial markets. Many media from the financial sphere more and more often publish information via video or even prefer this format in some cases. Manual analysis of these videos is very time-consuming. The work therefore deals with the creation of a tool enabling their automatic analysis. The work deals with two main areas. The first area is automatic speech recognition for obtaining transcripts of videos and the second area is natural language processing for performing text analysis on a given video. Text analysis includes sentiment analysis, text summarization and key phrase extraction.
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Mendel University performance analysis through data mining
Panggam, Osunam
This thesis explores the Mendel University performance analysis and the connection between the University ranking with the news articles and reviews. The study aims to analyze media coverage and review data on the universities over the years and their impact on the university's reputation and ranking. The research methodology involves web scraping news articles and reviews related to Mendel University and using data mining and NLP techniques to analyze their sentiment and topic distribution. Further, the qualitative data collected from news articles, online students’ reviews will be correlated with the University's ranking scores data over a past-years period to identify any patterns or relationships. The findings of the study will try to find insight into the impact of media coverage on university ranking and reputation. It will also shed light on the data mining techniques to analyze textual data related to the university for interesting patterns.
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Assessment and implementation of text data preprocessing in neural network models
Ratnasari, Febiyanti
In the realm of text data processing, text preprocessing has traditionally played a significant role. However, with the growing prominence of neural network models and novel representations of textual data, the importance of text preprocessing has been relatively understated. To address this, the present research endeavors to investigate the potential benefits of employing a composite of multiple text data preprocessing techniques in conjunction with a neural network-based text processing model.
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Extrakce informací ze zpráv o finančním stavu společností
Gramatová, Nikola
Investors strive to predict stock market returns, but it is a challenging task. Re-searchers have proposed various strategies as potential ways to forecast stock returns. Recently, data analytics and natural language processing have emerged as promising methods. This project aims to explore the use of natural language processing to predict changes in stock prices. The focus is on analyzing firms' 10-K and 10-Q reports to identify sentiment.
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Metasearch for Reviews on the Czech Web
Šmahel, Michal ; Doležal, Jan (referee) ; Smrž, Pavel (advisor)
The main purpose of this work is to create a metasearch engine for review articles with built-in sentiment analysis. In addition, a complex survey of main text extraction tools and web browser automation tools for web crawling has been carried out to achieve of the best possible results. The resulting metasearch engine provides a web interface for searching relevant review articles, thus saving time spent on manual searching. Thanks to multi-level transformer-based filtering, it can return 10—15 relevant review articles on frequently reviewed topics in about 4 minutes with no effort, just by clicking on a button.
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