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Social information discovery enhanced by sentiment analysis techniques.

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Title: Social information discovery enhanced by sentiment analysis techniques.
Authors: Diamantini, Claudia1 (AUTHOR) c.diamantini@univpm.it, Mircoli, Alex1 (AUTHOR) a.mircoli@pm.univpm.it, Potena, Domenico1 (AUTHOR) d.potena@univpm.it, Storti, Emanuele1 (AUTHOR) e.storti@univpm.it
Source: Future Generation Computer Systems. Jun2019, Vol. 95, p816-828. 13p.
Subjects: Sentiment analysis, Content analysis, Social networks, User-generated content, Open innovation, Information storage & retrieval systems
Abstract: Abstract In recent years, the massive diffusion of social networks has made available a large amount of user-generated content, for the most part in the form of textual data that contain people's thoughts and emotions about a great variety of topics. In order to exploit these publicly available information, in this work we introduce a social information discovery system which elaborates simultaneously over more-than-one social network in an integrated scenario. The system is designed to ensure flexibility and scalability, thus enabling for (near-)real-time analysis even in case of high rates of content's creation and large amounts of heterogeneous data. Furthermore, a noise detection technique ensures a high relevance of analyzed posts/tweets to the domain of interest. We also propose a lexicon-based sentiment analysis algorithm to extract and measure users' opinion, in order to support collaboration and open innovation. Polysemous words and negations are typically challenging for lexicon-based approaches: for this reason, we introduce both a word sense disambiguation algorithm and a negation handling technique. Experiments on several datasets have proven that the combined use of both techniques improves the classification accuracy on 3-class sentiment analysis. Highlights • Social networks provide a large amount of analyzable user-generated content (USG). • Social data are mainly textual and their analysis is complex. • Approach: a social information discovery system enhanced by sentiment analysis. • Dedicated algorithms for word sense disambiguation and negation handling improve the accuracy of sentiment analysis. • Experimental evidence of efficiency and effectiveness on synthetic and real data. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:Abstract In recent years, the massive diffusion of social networks has made available a large amount of user-generated content, for the most part in the form of textual data that contain people's thoughts and emotions about a great variety of topics. In order to exploit these publicly available information, in this work we introduce a social information discovery system which elaborates simultaneously over more-than-one social network in an integrated scenario. The system is designed to ensure flexibility and scalability, thus enabling for (near-)real-time analysis even in case of high rates of content's creation and large amounts of heterogeneous data. Furthermore, a noise detection technique ensures a high relevance of analyzed posts/tweets to the domain of interest. We also propose a lexicon-based sentiment analysis algorithm to extract and measure users' opinion, in order to support collaboration and open innovation. Polysemous words and negations are typically challenging for lexicon-based approaches: for this reason, we introduce both a word sense disambiguation algorithm and a negation handling technique. Experiments on several datasets have proven that the combined use of both techniques improves the classification accuracy on 3-class sentiment analysis. Highlights • Social networks provide a large amount of analyzable user-generated content (USG). • Social data are mainly textual and their analysis is complex. • Approach: a social information discovery system enhanced by sentiment analysis. • Dedicated algorithms for word sense disambiguation and negation handling improve the accuracy of sentiment analysis. • Experimental evidence of efficiency and effectiveness on synthetic and real data. [ABSTRACT FROM AUTHOR]
ISSN:0167739X
DOI:10.1016/j.future.2018.01.051