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Sentiment Analysis in Automotive Industry
Bezák, Adam ; Otrusina, Lubomír (referee) ; Smrž, Pavel (advisor)
The main theme of this thesis is to familiarize with the basic methods of sentiment analysis on social networks. Thesis’s theme is aimed on the automotive industry, although this prinicipal can be used in any different examined branch. The basis of the practical part is to obtain data from the social networks, analyze them and then index them into ElasticSearch database. Another goal of the thesis is to visualize these data by means of a web portal. Created web portal provides various statistics of the leading automobile brands, an overview of new trends or the aspect visualization of the individual cars.
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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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Rating of IT services through analysis of unstructured data
Kovykov, Maxim ; Vencovský, Filip (advisor) ; Bruckner, Tomáš (referee)
The main topic of this thesis is text mining and rating of services through summarization of unstructured text. The main goal is to describe a method for service rating. The method will be based on previous research. Described method will then be applied to real data. Another goal is to provide description of a toolset, necessary to fulfill set goals. This toolset will then be used to implement described method. The main contribution of this thesis is the implementation and application of the method on real data. The thesis is split into two parts: theory and practical application. Outputs of the practical applicaton are provided as an appendix.
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The influence of YouTube on users
Houdková, Eliška ; Truhlář, Filip (advisor) ; Čupak, Daniel (referee)
This bachelor thesis examines the impact YouTube has on its viewers. The goal of this thesis is to determine, what kind of video content has the most emotional influence on the viewers. Firstly, the data for the analysis was taken from the comment section of selected videos, secondly the sentiment analysis was executed. This determined the emotional subtext of said comments. The theoretical part of this thesis defines YouTube in contrast to other social networks, describes general types of YouTube channels and sums up basic psychological knowledge related to media psychology. Results of this thesis can be used by YouTube content creators, who want to improve their content. The analysis can be used to further examine behaviour of social network users as well.
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Application of text mining in analysis of current political situation
Jirků, Václav ; Jelínek, Ivan (advisor) ; Dražil, Michal (referee)
This thesis deals with the analysis of the political situation on Czech Facebook during the first three months of the year 2017. This analysis uses comments from Facebook users. Thesis has three main objectives. The first is defining the problem that is going to be solved. Second is analyzing current situation on Czech political Facebook and the third one is creating recommendations for politicians based on results of analysis of their specific actions. The thesis is divided into theoretical and practical part. The theoretical part explains concepts of text mining and sentiment analysis and introduce Czech political parties. A platform for analysis is also presented. In the practical part questions for analysis are defined and analysis, which is searching for answers to defined questions, is being performed. These questions are divided into two parts - general questions about situation and analysis of specific actions of politicians. Based on this analysis, it is possible to predict reaction of Facebook users on action of political parties or politician.
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Detection of Intensity in Sentiment Analysis of Czech
Dargaj, Jakub ; Tamchyna, Aleš (advisor) ; Mareček, David (referee)
Sentiment analysis is concerned with automatic extraction of subjective information from text. The goal of this thesis is to predict the intensity of attitude in Czech texts. In order to solve this task, we prepared a dataset of movie reviews by users of Czech-Slovak Film Database. We compare several machine learning methods, focusing on feature extraction from text data. Using convolutional neural networks and corpus-dependent training of word embeddings, we surpassed basic models and achieved accuracy similar to the most recent results in this field. We also analyze the logistic regression model in order to compare the vocabulary used in reviews with different ratings.
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Presidential rhetoric, sentiment and their relation to stock markets
Partelová, Mária ; Šopov, Boril (advisor) ; Žigraiová, Diana (referee)
This thesis intends to uncover the linkages between the emotions contained within remarks of the president of the United States expressed on Twitter and movements of the stock market indices. The daily comments of the two consecutive presidents, Barack Obama and Donald Trump are annotated with sentiment intensity values using the lexicon-based model called VADER. Our analysis further focuses on testing for Granger causality using the bivariate vector autoregression. Overall, three major stock market indices are employed in testing, namely DJIA, S&P 500 and NASDAQ. The results yield a statistically significant Granger causal relationship in the case of the first differences of DJIA and S&P 500 logarithms with time series of Barack Obama's sentiment values.
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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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On the Linguistic Structure of Emotional Meaning in Czech
Veselovská, Kateřina ; Hajičová, Eva (advisor) ; Petkevič, Vladimír (referee) ; Smrž, Pavel (referee)
Title: On the Linguistic Structure of Emotional Meaning in Czech Author: Mgr. Kateřina Veselovská Department: Institute of Formal and Applied Linguistics Supervisor: Prof. PhDr. Eva Hajičová, DrSc., Institute of Formal and Applied Linguistics Keywords: emotional meaning, linguistic structure, sentiment analysis, opinion mining, evaluative language Abstract: This thesis has two main goals. First, we provide an analysis of language means which together form an emotional meaning of written utterances in Czech. Sec- ond, we employ the findings concerning emotional language in computational applications. We provide a systematic overview of lexical, morphosyntactic, semantic and pragmatic aspects of emotional meaning in Czech utterances. Also, we propose two formal representations of emotional structures within the framework of the Prague Dependency Treebank and Construction Grammar. Regarding the computational applications, we focus on sentiment analysis, i.e. automatic extraction of emotions from text. We describe a creation of manually annotated emotional data resources in Czech and perform two main sentiment analysis tasks, polarity classification and opinion target identification on Czech data. In both of these tasks, we reach the state-of-the-art results.
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