Stance detection and summarization in social networks

During recent years, there have been a lot of research in the area of Natural Language Processing (NLP) related to the sentiment analysis. Stance detection goes even further and tries to detect whether the author of the text is in favor or against a given target. The main difference to sentiment analysis is that in stance detection, systems are to determine the author’s favorability towards a given target and the target may not even be explicitly mentioned in the text. Moreover, the text may express positive opinion about an entity contained in the text, but one can also infer that the author is against the defined target (an entity or a topic). This thesis is focused on the two main tasks: identifying the stance and its summarization and outlines the state-of-the-art approaches to stance detection and summarization. Copies of this report are available on http://www.kiv.zcu.cz/publications/ or by surface mail on request sent to the following address: University of West Bohemia in Pilsen Department of Computer Science and Engineering Univerzitni 8 30614 Pilsen Czech Republic Copyright c ©2018 University of West Bohemia in Pilsen, Czech Republic

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