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Multi-Label Classification of Text Documents
Průša, Petr ; Očenášek, Pavel (referee) ; Bartík, Vladimír (advisor)
The master's thesis deals with automatic classifi cation of text document. It explains basic terms and problems of text mining. The thesis explains term clustering and shows some basic clustering algoritms. The thesis also shows some methods of classi fication and deals with matrix regression closely. Application using matrix regression for classifi cation was designed and developed. Experiments were focused on normalization and thresholding.
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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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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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Dolování znalostí z textových dat související s migrační krizí
Koukal, Filip
This thesis focuses on the usage of machine learning techniques for knowledge mining from text data associated with the migrant crisis. Used data consists of articles and their comments downloaded from idnes.cz, an online news portal. This thesis explores the abilities of Word2Vec in relation to knowledge mining. A number of experiments that focus on the identification and characterization of topics embedded inside the downloaded articles were defined and carried out.
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Získávání skrytých znalostí z online dat souvisejících s vysokými školami
Hlaváč, Jakub
Social networks are a popular form of communication. They are also used by universities in order to simplify information providing and addressing candidates for study. Foreign study stays are also a popular form of education. Students, however, encounter a number of obstacles. The results of this work can help universities make their social network communication more efficient and better support foreign studies. In this work, the data from Facebook related to Czech universities and the Erasmus program questionnaire data were analyzed in order to find useful knowledge. The main emphasis was on textual content of communication. The statistical and machine learning methods, including mostly feature selection, topic modeling and clustering were used. The results reveal interesting and popular topics discussed on Czech universities social networks. The main problems of students related to their foreign studies were identified too and some of them were compared for countries and universities.
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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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|
|
Multi-Label Classification of Text Documents
Průša, Petr ; Očenášek, Pavel (referee) ; Bartík, Vladimír (advisor)
The master's thesis deals with automatic classifi cation of text document. It explains basic terms and problems of text mining. The thesis explains term clustering and shows some basic clustering algoritms. The thesis also shows some methods of classi fication and deals with matrix regression closely. Application using matrix regression for classifi cation was designed and developed. Experiments were focused on normalization and thresholding.
|
|
|
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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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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