|
| |
|
|
How to Create Self-Driven Education: The Social Web & Social Sciences, Coursera & Khan Academy 2014 Case Study
Růžička, Jakub ; Remr, Jiří (advisor) ; Soukup, Petr (referee)
This diploma thesis is concerned with the possibilities of the social web data employment in social sciences. Its theoretical part describes the changes in education in the context of the dynamics of contemporary society within three fundamental (interrelated) dimensions of technology (the cause and/or the tool for the change), work (new models of collaboration), and economics (sustainability of free & open-source business models). The main methodological part of the thesis is focused on the issues of sampling, sample representativeness, validity & reliability assessment, ethics, and data collection of the emerging social web research in social sciences. The research part includes illustrative social web analyses and conclusions of the author's 2014 Coursera & Khan Academy on the Social Web research and provides the full research report in its attachement to compare its results to the theoretical part in order to provide a "naive" (as derived from the social web mentions and networks) answer to the fundamental question: "How to Create Self-Driven Education?" Powered by TCPDF (www.tcpdf.org)
|
|
|
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.
|
|
|
Machine-Learning Methods in Natural Language Processing
Vodička, Jan ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
Bachelor's thesis deals with sentiment analysis using machine learning methods, mainly naive bayes classifier. Input text can be classified as positive or negative message. There are used several data sources for create of automatic annotated corpus - social network Twitter, price comparator Heureka, movie database ČSFD and restaurant portal Scuk. These sources are compared in terms of performance in assessing the sentiment. Consequently, the final training dataset is created and used at almost real-time Twitter sentiment analysis.
|
|
|
Hadoop and Business Intelligence
Kerner, Josef ; Šperková, Lucie (advisor) ; Augustín, Jakub (referee)
The main purpose of this thesis is to describe how an integration of a Hadoop platform into currently existing Business Intelligence technologies and processes can augment its data processing and analysis capabilities while encountering Big Data. Furthermore, it describes reasons why the whole Hadoop application ecosystem was founded and informs the reader about the functionality of its primary components. It continues with provision of overview about Hadoop higher-level components architecture and their use in existing Business Intelligence processes such as data ingestion, transformation and analysis. In the last theoretical chapter it focuses itself on describing specific areas of utilization of the Hadoop platform and Big Data in data warehousing, text mining and predictive analytics. From the practical point of view, a particular use case is provided, an implementation of Big Data ETL process in the field of financial markets and trading with a detailed explanation of the corresponding necessities such as data model, ETL code and proposed metrics, which can be further implemented for achieving increased return on investments.
|
|
|
Sentiment Analysis in the Czech Environment Network Twitter
Koller, Michael ; Kincl, Tomáš (advisor) ; Novák, Michal (referee)
Opinions are key influencers of human behaviours. Therefore, this bachelor thesis focuses on sentiment analysis, also called opinion mining, which is the one of the most active research areas in natural language processing and its application in business intelligence is more and more significant. Theoretical part thus describes the sentiment analysis problem, its advantages and applications. The main purpose of this thesis is to describe and appraise available tools for sentiment analysis in Czech conditions with focus on Twitter.
|
|
|
Sentiment analysis
Pelíšek, Jiří ; Kincl, Tomáš (advisor) ; Novák, Michal (referee)
The presented bachelor thesis describes possibilities of the application of sentiment analysis and compares the effectiveness of several tools used in sentiment analyses. First is described the importance of sentiment analysis in marketing. Further on there the term sentiment analysis is fully defined. In the following section basic concepts are explained and various methods described. The last chapter compares several services providing tools for sentiment analysis.
|
|
|
Sentiment analysis of social networks
Zaplatílek, Jan ; Jelínek, Ivan (advisor) ; Bruckner, Tomáš (referee)
This thesis concerns about sentiment analysis. In more detail sentiment analysis of social networks. Main goal of sentiment analysis is determine if tested document expresses any sentiment and, if so, whether is positive or negative. Main reason for sentiment analysis on social networks is detecting sentiment and feels about some company or brand. This activity is called brand monitoring. Information acquired from brand monitoring can be used for improving marketing or communication with customers. This thesis deals with sentiment analysis of post from public Facebook profiles of several Czech banks and telecommunication operators. Goal of this thesis is create model which has precision of determine sentiment of Facebook posts at least 80%. Method for achieving this goal is experiment. First part of this thesis describes sentiment analysis theory, definition of sentiment analysis, its problems, methods, reasons for use and use cases of sentiment analysis. Second part gives background research of often used methods and data sources for sentiment analysis in foreign research. Finally third part of this theses describes experiment, its preparation and results. Main benefit of this theses is creating model which can be later use in real word.
|
|
|
Automated Sentiment Analysis
Zeman, Matěj ; Kincl, Tomáš (advisor) ; Přibil, Jiří (referee)
The goal of my master thesis is to describe the Automated Sentiment Analysis, its methods and Cross-domain problems and to test the already existing model. I have applied this model on the data from the Czech-Slovak film database website CSFD.cz, Czech e-shop MALL.cz and one of the biggest Czech websites about books Databazeknih.cz to contribute to the solution of the Cross-Domain issue by using n-grams and the analytic software RapidMiner.
|
|
|
Competitive analysis of leading ICT companies on the Czech market
Dvořák, Oskar ; Feige, Tomáš (advisor) ; Molnár, Zdeněk (referee)
This thesis deals with the field of Competitive Intelligence in relation to the possibilities of application of its methods and tools for competitive analysis of the market environment using modern virtual social networks. Theoretical part focuses on the characteristics of the market environment of ICT companies by using Porter's analysis and then it is focused on the description of selected tools and methods used to processing unstructured data and social networks analysis. The practical part is based on a real project which ran from early March 2013 at IBM Company. Practical part demonstrates current possibilities of information technology in the field of Competitive Intelligence.
|
guest ::
