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Sentiment Analysis of Czech Social Networks and Web Discussions on Retail Chains
Bolješik, Michal ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
The goal of this thesis is to design and implement a system that analyses data from the web mentioning Czech grocery chain stores. Implemented system is able to download such data automatically, perform sentiment analysis of the data, extract locations and chain stores' names from the data and index the data. The system also includes a user interface showing results of the analyses. The first part of the thesis surveys the state of the art in collecting data from web, sentiment analysis and indexing documents. A description of the discussed system's design and its implementation follows. The last part of the thesis evaluates implemented system
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Searching in Speech Data
Fapšo, Michal ; Černocký, Jan (referee) ; Szőke, Igor (advisor)
This thesis describes a designed and implemented system for efficient storage, indexing and search in collections of spoken documents that takes advantage of automatic speech recognition. As the quality of current speech recognizers is not sufficient for a great deal of applications, it is necessary to index the ambiguous output of the recognition, i.\,e. the acyclic graphs of word hypotheses -- recognition lattices. Then, it is not possible to directly apply the standard methods known from text--based systems. This paper discusses an optimized indexing system for efficient search in the complex and large data structures which are the output of the recognizer.
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Modification of Pivot Tables method for persistent metric indexing
Moško, Juraj ; Skopal, Tomáš (advisor) ; Hoksza, David (referee)
The pivot tables is one of the most effective metric access method optimized for a number of distance computations in similarity search. In this work the new modification of the pivot tables method was proposed that is besides distance computations optimized also for a number of I/O operations. Proposed Clustered pivot tables method is indexing clusters of similar objects that were created by another metric access method - the M-tree. The indexing of clustered objects has a positive effect for searching within indexed database. Whereas the clusters are paged in second memory, page containing such cluster, which do not satisfy particular query, is not accessed in second memory at all. Non-relevant objects, that are out of the query range, are not loaded into memory, what has the effect of decreasing number of I/O operations and total volume of transferred data. The correctness of proposed approach was experimentally proved and experimental results of proposed method was compared to selected metric access methods.
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Sentiment Analysis of Czech Social Networks and Web Discussions on Retail Chains
Bolješik, Michal ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
The goal of this thesis is to design and implement a system that analyses data from the web mentioning Czech grocery chain stores. Implemented system is able to download such data automatically, perform sentiment analysis of the data, extract locations and chain stores' names from the data and index the data. The system also includes a user interface showing results of the analyses. The first part of the thesis surveys the state of the art in collecting data from web, sentiment analysis and indexing documents. A description of the discussed system's design and its implementation follows. The last part of the thesis evaluates implemented system
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Modification of Pivot Tables method for persistent metric indexing
Moško, Juraj ; Skopal, Tomáš (advisor) ; Hoksza, David (referee)
The pivot tables is one of the most effective metric access method optimized for a number of distance computations in similarity search. In this work the new modification of the pivot tables method was proposed that is besides distance computations optimized also for a number of I/O operations. Proposed Clustered pivot tables method is indexing clusters of similar objects that were created by another metric access method - the M-tree. The indexing of clustered objects has a positive effect for searching within indexed database. Whereas the clusters are paged in second memory, page containing such cluster, which do not satisfy particular query, is not accessed in second memory at all. Non-relevant objects, that are out of the query range, are not loaded into memory, what has the effect of decreasing number of I/O operations and total volume of transferred data. The correctness of proposed approach was experimentally proved and experimental results of proposed method was compared to selected metric access methods.
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Searching in Speech Data
Fapšo, Michal ; Černocký, Jan (referee) ; Szőke, Igor (advisor)
This thesis describes a designed and implemented system for efficient storage, indexing and search in collections of spoken documents that takes advantage of automatic speech recognition. As the quality of current speech recognizers is not sufficient for a great deal of applications, it is necessary to index the ambiguous output of the recognition, i.\,e. the acyclic graphs of word hypotheses -- recognition lattices. Then, it is not possible to directly apply the standard methods known from text--based systems. This paper discusses an optimized indexing system for efficient search in the complex and large data structures which are the output of the recognizer.
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