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The algorithm for the detection of positive and negative text
Musil, David ; Harár, Pavol (referee) ; Povoda, Lukáš (advisor)
As information and communication technology develops swiftly, amount of information produced by various sources grows as well. Sorting and obtaining knowledge from this data requires significant effort which is not ensured easily by a human, meaning machine processing is taking place. Acquiring emotion from text data is an interesting area of research and it’s going through considerable expansion while being used widely. Purpose of this thesis is to create a system for positive and negative emotion detection from text along with evaluation of its performance. System was created with Java programming language and it allows training with use of large amount of data (known as Big Data), exploiting Spark library. Thesis describes structure and handling text from database used as source of input data. Classificator model was created with use of Support Vector Machines and optimized by the n-grams method.
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Preprocessing and Transformation of Text Data Collections
Maruna, Viktor ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This bachelor thesis deals with the issue of text-mining, mostly focused on preprocessing and transformation. In theoretical part there are contained information about development and principles of text-mining processes, text data collections and use in practice. The next part of this thesis describes in detail single steps of preprocessing and transformation of text data collections. In the final parts there are reviews of application development, testing and personal view on this thesis.
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Data mining from incoming e-mail messages
Šebesta, Jan ; Žemlička, Michal (advisor) ; Hnětynka, Petr (referee)
In the present work we study possibilities of automatic sorting of incoming email communication. Our primary goal is to distinguish information about oncoming workshops and conferences, job off ers and published books. We are trying to develop tool to mine the information from data from professional mailing lists. Off ers in the mailing lists come in html, rtf or plain text format, but the information in it is written in common spoken language. We are developing the system so it will use text mining methods to extract the information and save it structured form. Than we will be able to work with it. We are examining the handling of the mails by user and apply the knowledge in the development. We solve the problems with obtaining of the messages, distinguishing language and encoding and estimating the type of message. After recognition of the bearing information we are able to mine data. In the end we save the mined information to the database, which allows us to display it in well{arranged way, sort and search according to the user needs.
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Data mining from incoming e-mail messages
Šebesta, Jan ; Žemlička, Michal (advisor) ; Kopecký, Michal (referee)
We study possibilities of automatic sorting of incoming e-mails. Our primary goal is to distinguish information about oncoming workshops and conferences, job offers and published books. We are developing mining tool for extracting the information from data originated in profession-specific mailing lists. Offers in the mailing lists come in html, rtf or plain text format. The messages are written in common spoken language. We have developed the system so it will use text mining methods to extract the information and save it structured form. Then we will be able to work with it. We are examining how user handles the mail and apply the knowledge in the development. We solve the problems with obtaining of the messages, distinguishing language and encoding and estimating the type of message. After recognition of the transported information we are able to mine data. In the end we save the mined information to the database, which allows us to display it in well-arranged way, sort and search according to the user needs.
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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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Data mining from incoming e-mail messages
Šebesta, Jan ; Žemlička, Michal (advisor) ; Kopecký, Michal (referee)
We study possibilities of automatic sorting of incoming e-mails. Our primary goal is to distinguish information about oncoming workshops and conferences, job offers and published books. We are developing mining tool for extracting the information from data originated in profession-specific mailing lists. Offers in the mailing lists come in html, rtf or plain text format. The messages are written in common spoken language. We have developed the system so it will use text mining methods to extract the information and save it structured form. Then we will be able to work with it. We are examining how user handles the mail and apply the knowledge in the development. We solve the problems with obtaining of the messages, distinguishing language and encoding and estimating the type of message. After recognition of the transported information we are able to mine data. In the end we save the mined information to the database, which allows us to display it in well-arranged way, sort and search according to the user needs.
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Data mining from incoming e-mail messages
Šebesta, Jan ; Žemlička, Michal (advisor) ; Hnětynka, Petr (referee)
In the present work we study possibilities of automatic sorting of incoming email communication. Our primary goal is to distinguish information about oncoming workshops and conferences, job off ers and published books. We are trying to develop tool to mine the information from data from professional mailing lists. Off ers in the mailing lists come in html, rtf or plain text format, but the information in it is written in common spoken language. We are developing the system so it will use text mining methods to extract the information and save it structured form. Than we will be able to work with it. We are examining the handling of the mails by user and apply the knowledge in the development. We solve the problems with obtaining of the messages, distinguishing language and encoding and estimating the type of message. After recognition of the bearing information we are able to mine data. In the end we save the mined information to the database, which allows us to display it in well{arranged way, sort and search according to the user needs.
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The algorithm for the detection of positive and negative text
Musil, David ; Harár, Pavol (referee) ; Povoda, Lukáš (advisor)
As information and communication technology develops swiftly, amount of information produced by various sources grows as well. Sorting and obtaining knowledge from this data requires significant effort which is not ensured easily by a human, meaning machine processing is taking place. Acquiring emotion from text data is an interesting area of research and it’s going through considerable expansion while being used widely. Purpose of this thesis is to create a system for positive and negative emotion detection from text along with evaluation of its performance. System was created with Java programming language and it allows training with use of large amount of data (known as Big Data), exploiting Spark library. Thesis describes structure and handling text from database used as source of input data. Classificator model was created with use of Support Vector Machines and optimized by the n-grams method.
|
|
|
Preprocessing and Transformation of Text Data Collections
Maruna, Viktor ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This bachelor thesis deals with the issue of text-mining, mostly focused on preprocessing and transformation. In theoretical part there are contained information about development and principles of text-mining processes, text data collections and use in practice. The next part of this thesis describes in detail single steps of preprocessing and transformation of text data collections. In the final parts there are reviews of application development, testing and personal view on this thesis.
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