A Latent Dirichlet Allocation Approach using Mixed Graph of Terms for Sentiment Analysis

dc.contributor.author Casillo, Mario
dc.contributor.author Clarizia, Fabio
dc.contributor.author Colace, Francesco
dc.contributor.author De Santo, Massimo
dc.contributor.author Lombardi, Marco
dc.contributor.author Pascale, Francesco
dc.date.accessioned 2019-01-03T00:01:57Z
dc.date.available 2019-01-03T00:01:57Z
dc.date.issued 2019-01-08
dc.description.abstract The spread of generic (as Twitter, Facebook orGoogle+) or specialized (as LinkedIn or Viadeo) social networks allows to millions of users to share opinions on different aspects of life every day. Therefore this information is a rich source of data for opinion mining and sentiment analysis. This paper presents a novel approach to the sentiment analysis based on the Latent Dirichlet Allocation (LDA) approach. The proposed methodology aims to identify a word-based graphical model (we call it a mixed graph of terms) for depicting a positive or negative attitude towards a topic. By the use of this model it will be possible to automatically mine from documents positive and negative sentiments.Experimental evaluation, on standard and real datasets, shows that the proposed approach is effective and furnishes good and reliable results.
dc.format.extent 9 pages
dc.identifier.doi 10.24251/HICSS.2019.270
dc.identifier.isbn 978-0-9981331-2-6
dc.identifier.uri http://hdl.handle.net/10125/59661
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Data Analytics, Data Mining and Machine Learning for Social Media
dc.subject Digital and Social Media
dc.subject Information Extraction, Latent Dirichlet Allocation, Mixed Graph of Terms, Relations Learning, Sentiment Analysis, Structure Learning.
dc.title A Latent Dirichlet Allocation Approach using Mixed Graph of Terms for Sentiment Analysis
dc.type Conference Paper
dc.type.dcmi Text
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