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Characterization of sentiment groups on Twitter

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dc.contributor.author Grozavu, Nistor
dc.contributor.author Rogovschi, Nicoleta
dc.date.accessioned 2019-10-22T08:53:03Z
dc.date.available 2019-10-22T08:53:03Z
dc.date.issued 2014
dc.identifier.citation Grozavu, Nistor, Rogovschi, Nicoleta. Characterization of sentiment groups on Twitter. In: Microelectronics and Computer Science: proc. of the 8th intern. conf., October 22-25, 2014. Chişinău, 2014, pp.202-203 . ISBN 978-9975-45-329-5. en_US
dc.identifier.isbn 978-9975-45-329-5
dc.identifier.uri http://repository.utm.md/handle/5014/4983
dc.description.abstract Opinion Mining is the field of computational study of people’s emotional behavior expressed in text. The purpose of this article is to introduce a new framework for characterization of the groups of emotions extracted from tweet data. In contrast to supervised learning, the problem of clustering characterization in the context of opinion mining based on unsupervised learning is challenging, because label information is not available. The proposed framework uses topological unsupervised learning and hierarchical clustering, each cluster being associated to a prototype and a weight vector, reflecting the relevance of the data belonging to each cluster. The proposed framework requires simple computational techniques and is based on the double local weighting self-organizing map (dlw-SOM) model and Hierarchical Clustering. The proposed framework has been used on a real dataset issued from the tweets collected during the 2012 French election campaign. en_US
dc.language.iso en en_US
dc.publisher Tehnica UTM en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject twitter en_US
dc.subject emotions mining en_US
dc.subject clustering en_US
dc.title Characterization of sentiment groups on Twitter en_US
dc.type Article en_US


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