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

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dc.contributor.authorCasillo, Mario
dc.contributor.authorClarizia, Fabio
dc.contributor.authorColace, Francesco
dc.contributor.authorDe Santo, Massimo
dc.contributor.authorLombardi, Marco
dc.contributor.authorPascale, Francesco
dc.date.accessioned2019-01-03T00:01:57Z
dc.date.available2019-01-03T00:01:57Z
dc.date.issued2019-01-08
dc.description.abstractThe 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.extent9 pages
dc.identifier.doi10.24251/HICSS.2019.270
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59661
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Data Mining and Machine Learning for Social Media
dc.subjectDigital and Social Media
dc.subjectInformation Extraction, Latent Dirichlet Allocation, Mixed Graph of Terms, Relations Learning, Sentiment Analysis, Structure Learning.
dc.titleA Latent Dirichlet Allocation Approach using Mixed Graph of Terms for Sentiment Analysis
dc.typeConference Paper
dc.type.dcmiText

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