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Automatic recognition of meaning in texts
Jeleček, Jiří ; Dvořák, Pavel (referee) ; Povoda, Lukáš (advisor)
As part of this work it was designed and implemented a system using data mining techniques from the text in order to detect emotions in Czech, English and German language texts. Because the system is built mostly on machine learning techniques, was designed and created training set, which was later used to build the model classifier using the selected algorithms.
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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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Algorithm for Detection of Positive and Negative Text
Musil, David
In the present, obtaining and sorting knowledge from data produced by various sources requires significant effort which is not ensured easily by a human, meaning machine processing is taking place. Purpose of this work was to create a system capable of positive and negative emotion detection from text along with evaluation of its performance. System allows training with use of large amount of data (known as Big Data), exploiting Spark library. Classificator model was created with use of Support Vector Machines. Highest achieved accuracy is 78,05% for Czech, 79,73% for German and 91,88% for English.
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Automatic recognition of meaning in texts
Jeleček, Jiří ; Dvořák, Pavel (referee) ; Povoda, Lukáš (advisor)
As part of this work it was designed and implemented a system using data mining techniques from the text in order to detect emotions in Czech, English and German language texts. Because the system is built mostly on machine learning techniques, was designed and created training set, which was later used to build the model classifier using the selected algorithms.
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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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