National Repository of Grey Literature 69 records found  1 - 10nextend  jump to record: Search took 0.00 seconds. 
Sentiment Analysis for the Field of Computer Games
Balajka, Pavel ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
The thesis deals with sentiment analysis extracted from opinions of users on social \mbox{networks}. It describes a general system that was created for presented purpose and specialised on the field of strategic computer games. In particular we unravel the problems of acquiring data from social networks, sentiment analysis and results presentation to the user. We mention particular ways of text processing e.g. tokenization and unnecessary word filtration, for purpose of more effective sentiment analysis and we mention machine learning methods e.g. Decision Trees and Naive Bayes, and their usage. Next we describe design of desired system and its implementation with chosen parts and methods. In the end we compare results of tests of sentiment analyzator done under various circumstances.
Semantic Similarity of Texts
Hajdin, Martin ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This paper deals with the determination of the semantic similarity of texts focusing on categorization of web documents in this case bookmarks. The part of the process is a theoretical overview of methods for system implementation. It describes the design and implementation of the various methods used in the system, too. This paper also deals with the evaluation of various methods where the chosen method are tested according to specified criteria.
Text Messages Manager for Android
Bloudíček, Jan ; Otrusina, Lubomír (referee) ; Kouřil, Jan (advisor)
This thesis describes a creation of an application for mobile devices which is designed to manage short text messages and electronic mail on the Android platform. Text messages can be sent also through the SMS gateways. This work explains basic concepts and technologies for developing applications for Android. It describes the analysis phase, design of architecture and user interface, implementation and testing of the program.
Module for Task Administration for NLPIS
Žurek, Aleš ; Otrusina, Lubomír (referee) ; Dytrych, Jaroslav (advisor)
This bachelor thesis analyzes the current information system NLPIS and its components. It include design and implementation that extends the capabilities of existing system NLPIS and adjusts its components. It aims to manage tasks and to link the system NLPIS with MediaWiki. The solution includes an analysis of the newly added classes.
Information Extraction from Wikipedia
Valušek, Ondřej ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This thesis deals with automatic type extraction in English Wikipedia articles and their attributes. Several approaches with the use of machine learning will be presented. Furthermore, important features like date of birth in articles regarding people, or area in those about lakes, and many more, will be extracted. With the use of the system presented in this thesis, one can generate a well structured knowledge base, using a file with Wikipedia articles (called dump file) and a small training set containing a few well-classed articles. Such knowledge base can then be used for semantic enrichment of text. During this process a file with so called definition words is generated. Definition words are features extracted by natural text analysis, which could be used also in other ways than in this thesis. There is also a component that can determine, which articles were added, deleted or modified in between the creation of two different knowledge bases.
Identifying Entity Types and Attributes Across Languages
Švub, Daniel ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
The target of this thesis is to analyze articles on the Wikipedia internet encyclopedia and to convert their text written in natural language into a structured database of persons, places and other entities. The essence of the implemented program is the determination of the type of entity based on its typical characteristics, and the extraction of the most important attributes of this entity in the Czech and Slovak languages. The result of this task is a knowledge base allowing simple searching and sorting of information. Thanks to its easy extensibility, it is possible to add identification of other types of entities and other features to the program, as well as a support of other languages.
Keyword Suggestion in the Central Portal of Czech Libraries
Balaga, Róbert ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This thesis deals with various methods of keyphrase extraction from documents, specifically focused on documents from the Central Portal of Czech Libraries. Various methods from statistical, linguistic and graph-based methods have been implemented. Also a new method was suggested, that combines the statistical and linguistic approach. Individual methods have been tested and analyzed according to the standard evaluation metrics, with the suggested method achieving recall of 30 percent.
Analysis of Social Media Content Discussing Czech Mobile Operators
Pavlů, Jan ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
The main topic of this thesis is sentiment analysis of posts obtained from a social networks. The posts are about czech mobile network operators. The essential part of implemented system is also data visualization. The sentiment analysis is done using machine learning techniques. Downloaded posts are cleaned, lemmatized and transformed to feature vectors. Stochastic Gradient Descent algorithm is used for classification. Analyzed data are visualized in charts and as the list of posts. The system provides tools for text categorization. The accuracy, precision, recall and F1 score of sentiment analysis is about 75%. The accuracy of post categorization is high (about 80%), but precision, recall and F1 score are low (about 30%). This is the reason why post categorization isn't automatically done. The benefit of the system it that it automatically collects data from different sources, analysis them and displays them. It also provides tools for manual change of sentiment/categories which can lead to better system characteristics with some help of users.
Identifying Entity Types Based on Information Extraction from Wikipedia
Rusiňák, Petr ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
This paper presents a system for identifying entity types of articles on Wikipedia (e.g. people or sports events) that can be used for identifaction of any arbitrary entity. The~input files for this system are a list of several pages that belong to this entity and a list of several pages that do not belong to this entity. These lists will be used to generate features that can be used for generation of the list of all pages belonging to this entity. The fatures can be based on both structured information on Wikipedia such as templates and categories and non-structured informations found by the analysis of natural text in the first sentence of the article where a defining noun that represents what the article is about will be found. This system support pages written in Czech and English and can be extended to support other languages.
Semantic Enrichment Component
Doležal, Jan ; Otrusina, Lubomír (referee) ; Dytrych, Jaroslav (advisor)
This master's thesis describes Semantic Enrichment Component (SEC), that searches entities (e.g., persons or places) in the input text document and returns information about them. The goals of this component are to create a single interface for named entity recognition tools, to enable parallel document processing, to save memory while using the knowledge base, and to speed up access to its content. To achieve these goals, the output of the named entity recognition tools in the text was specified, the tool for storing the preprocessed knowledge base into the shared memory was implemented, and the client-server scheme was used to create the component.

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