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Recognition of emotions in Czech texts
Červenec, Radek ; Smékal, Zdeněk (referee) ; Burget, Radim (advisor)
With advances in information and communication technologies over the past few years, the amount of information stored in the form of electronic text documents has been rapidly growing. Since the human abilities to effectively process and analyze large amounts of information are limited, there is an increasing demand for tools enabling to automatically analyze these documents and benefit from their emotional content. These kinds of systems have extensive applications. The purpose of this work is to design and implement a system for identifying expression of emotions in Czech texts. The proposed system is based mainly on machine learning methods and therefore design and creation of a training set is described as well. The training set is eventually utilized to create a model of classifier using the SVM. For the purpose of improving classification results, additional components were integrated into the system, such as lexical database, lemmatizer or derived keyword dictionary. The thesis also presents results of text documents classification into defined emotion classes and evaluates various approaches to categorization.
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Improved Prediction of Social Tags Using Data Mining
Harár, Pavol ; Galáž, Zoltán (referee) ; Kříž, Jiří (advisor)
This master’s thesis deals with using Text mining as a method to predict tags of articles. It describes the iterative way of handling big data files, parsing the data, cleaning the data and scoring of terms in article using TF-IDF. It describes in detail the flow of program written in programming language Python 3.4.3. The result of processing more than 1 million articles from Wikipedia database is a dictionary of English terms. By using this dictionary one is capable of determining the most important terms from article in corpus of articles. Relevancy of consequent tags proves the method used in this case.
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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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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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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.
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Detekce kategorie obsahu webové stránky prostřednictvím metod strojového učení.
DOHNAL, Patrik
This bachelor thesis is focused on design and the implementation of the algorithm for classifying the websites into a several categories. The implementation of this software is written in Python. For classifying purposes I use machine learning models such as Naive Bayes classifier, K-Nearest neighbors and Support Vector Machines. Within the process it is assumed to collect my own dataset, wich will be used for training and testing purposes. Thesis also includes detailed description of the methods I uesd.
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Knowledge Discovery from Text Data in the Python Language
Homola, Ján ; Hynek, Jiří (referee) ; Bartík, Vladimír (advisor)
This bachelor thesis deals with knowledge discovery from text data more specifically classification of text-based user reviews. Using experiments, this thesis focuses on methods for preprocessing text data and comparing different classification methods through selected datasets. The conclusion of the work is the evaluation of the achieved results of experiments that were performed using the implemented application.
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