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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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Artificial Neural Network for Opinion Target Identification in Czech
Glončák, Vladan ; Kuboň, Vladislav (advisor) ; Mírovský, Jiří (referee)
The main focus of this thesis is to use neural networks, specifically long short-term memory cells, for identifying opinion targets in Czech data. The side product is a new version of dataset for opinion target identification. For a comparison, previously obtained results for another languages and by employing probabilistic methods instead were listed. The experiment was successful, achieved results are above trivial baseline models and comparable with the results achieved previously. Powered by TCPDF (www.tcpdf.org)
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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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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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Artificial Neural Network for Opinion Target Identification in Czech
Glončák, Vladan ; Kuboň, Vladislav (advisor) ; Mírovský, Jiří (referee)
The main focus of this thesis is to use neural networks, specifically long short-term memory cells, for identifying opinion targets in Czech data. The side product is a new version of dataset for opinion target identification. For a comparison, previously obtained results for another languages and by employing probabilistic methods instead were listed. The experiment was successful, achieved results are above trivial baseline models and comparable with the results achieved previously. Powered by TCPDF (www.tcpdf.org)
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