A lightweight clustering–based approach to discover different emotional shades from social message streams

International Journal of Intelligent SystemsVolume 34, Issue 7 p. 1505-1523 RESEARCH ARTICLE A lightweight clustering–based approach to discover different emotional shades from social message streams Ferdinando Di Martino, Corresponding Author Ferdinando Di Martino fdimarti@unina.it Dipartimento di Architettura, Università degli Studi di Napoli Federico II, Napoli, Italy Correspondence Ferdinando Di Martino, Dipartimento di Architettura, Università degli Studi di Napoli Federico II, Via Toledo 402, 80134 Napoli, Italy. Email: fdimarti@unina.itSearch for more papers by this authorSabrina Senatore, Sabrina Senatore Dipartimento di Ingegneria dell'Informazione ed Elettrica e Matematica applicata, Università degli Studi di Salerno, Fisciano, Salerno, ItalySearch for more papers by this authorSalvatore Sessa, Salvatore Sessa Dipartimento di Architettura, Università degli Studi di Napoli Federico II, Napoli, ItalySearch for more papers by this author Ferdinando Di Martino, Corresponding Author Ferdinando Di Martino fdimarti@unina.it Dipartimento di Architettura, Università degli Studi di Napoli Federico II, Napoli, Italy Correspondence Ferdinando Di Martino, Dipartimento di Architettura, Università degli Studi di Napoli Federico II, Via Toledo 402, 80134 Napoli, Italy. Email: fdimarti@unina.itSearch for more papers by this authorSabrina Senatore, Sabrina Senatore Dipartimento di Ingegneria dell'Informazione ed Elettrica e Matematica applicata, Università degli Studi di Salerno, Fisciano, Salerno, ItalySearch for more papers by this authorSalvatore Sessa, Salvatore Sessa Dipartimento di Architettura, Università degli Studi di Napoli Federico II, Napoli, ItalySearch for more papers by this author First published: 04 March 2019 https://doi.org/10.1002/int.22105Citations: 19Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Abstract With the explosion of social media, automatic analysis of sentiment and emotion from user-generated content has attracted the attention of many research areas and commercial-marketing domains targeted at studying the social behavior of web users and their public attitudes toward brands, social events, and political actions. Capturing the emotions expressed in the written language could be crucial to support the decision-making processes: the emotion resulting from a tweet or a review about an item could affect the way to advertise or to trade on the web and then to make predictions about future changes in popularity or market behavior. This paper presents an experience with the emotion-based classification of textual data from a social network by using an extended version of the fuzzy C-means algorithm called extension of fuzzy C-means. The algorithm shows interesting results due to its intrinsic fuzzy nature that reflects the human feeling expressed in the text, often composed of a mix of blurred emotions, and at the same time, the benefits of the extended version yield better classification results. Citing Literature Volume34, Issue7July 2019Pages 1505-1523 RelatedInformation