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Artificial neural networks and their application in text analysis
Jankovič, Radovan ; Mrázová, Iveta (advisor) ; Neruda, Roman (referee)
This thesis is devoted to the area of sentiment analysis. Its goal is to discuss and compare various methods applicable to sentiment classification of short texts. When analyzing the described techniques, we will orient ourselves towards the context of social networks. Recently, this type of media became the source of vast amounts of data and the demand for its automatic processing is high. Interesting results have been obtained for clustering used in combination with supervised learning and convolution, which is primarily used for image data.
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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)
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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.
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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.
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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.
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Virtual Image of the Czech Sports via Unstructured Data Analysis
Levý, Jan ; Jelínek, Ivan (advisor) ; Profousová, Lenka (referee)
Analysis of unstructured data from social media is an important and constantly growing part of information technologies' application in the marketing context. The aims of this bachelors' thesis include creating a trial platform for the analysis of un-structured data from Facebook, research and application of suitable methods and metrics for the analysis, summarizing the analysis' findings and visualisation of selected metrics by means of a dashboard. The thesis' structure corresponds to the methods used for metting the aims set and compo-ses of seven parts. The first part summarizes motives for social media analysis, deals with the approach to the topic in the contemporary scientific papers and discusses comercial tools that could be used for such analysis. The second part describes gathering of data with a crawler, setting the connection and getting the access rights to the data sources. The structure of downloaded documents and appropriate choice of data sources is also described in this part. The third part adresses the tools used for the analysis, namely Elas-ticsearch and Kibana. The fourth part defines possible questions that may be asked in the context of Czech sports image on Facebook. The fifth part identifies the metrics necessary for the questions assesment and describes corresponding data selection. The sixth part completes the analysis itself and summarizes its findnings. The seventh part describes Ki-bana's dashboard use for effective summarizing of selected metrics. The main bachelors' thesis contribution lies in the illustration of social media analysis pos-sibilities on the example of Czech sportsmen and Czech sports in general on Facebook. A crawler has been used to gather the data for the purpose of the analysis. The other em-ployed tools included Elasticsearch and Kibana, which have enabled the data selection and visualisation. The analytical approach consisted of four parts; definition of analytical que-stions, definition of methods and metrics to find answers to these questions, followed by the analysis itself and the summary of results. The following points belong to the analysis' findnings: identification of sportsman with the highest marketing potential, comparison of analysed sports according to the perceived image by fans and desgination of the pages with most interacting followers.
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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.
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Aplikace metod strojového učení na dolování znalosti z dat
Kraus, Jan
The diploma thesis deals with the area of data mining applied to large collections of textual data. Specifically the thesis is focused on sentiment analysis based on the user's subjective verbal assessment in natural language. The first part of the diploma thesis introduces the reader to basic terms of machine learning and data mining applied particularly to large textual data collections. Following is the description of textual data preprocessing methods and principles of machine learning algorithms. In the practical part of this thesis there are experiments designed and subsequently executed using the SPSS Modeler tool. The experimental part is focused especially on identification of significant attributes and recongnition of relationships between them. The emphasis is put especially on thorough interpretation of the results obtained.
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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.
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Big data - application in banking
Uřídil, Martin ; Slánský, David (advisor) ; Pour, Jan (referee)
There is a growing volume of global data, which is offering new possibilities for those market participants, who know to take advantage of it. Data, information and knowledge are new highly regarded commodity especially in the banking industry. Traditional data analytics is intended for processing data with known structure and meaning. But how can we get knowledge from data with no such structure? The thesis focuses on Big Data analytics and its use in banking and financial industry. Definition of specific applications in this area and description of benefits for international and Czech banking institutions are the main goals of the thesis. The thesis is divided in four parts. The first part defines Big Data trend, the second part specifies activities and tools in banking. The purpose of the third part is to apply Big Data analytics on those activities and shows its possible benefits. The last part focuses on the particularities of Czech banking and shows what actual situation about Big Data in Czech banks is. The thesis gives complex description of possibilities of using Big Data analytics. I see my personal contribution in detailed characterization of the application in real banking activities.
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