A lightweight clustering–based approach to discover different emotional shades from social message streams. Issue 7 (4th March 2019)

• Record Type: Journal Article
• Authors: Di Martino, Ferdinando; Senatore, Sabrina; Sessa, Salvatore
• Is Part Of: International journal of intelligent systems. Volume 34:Issue 7(2019)
• Page Start: 1505
• Page End: 1523
• Publication Date: 2019-03-04
• Subjects: EFCM -- emotion extraction -- FCM -- sentic computing -- sentiment analysis; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22105
• Languages: English
• ISSNs: 0884-8173

Abstract

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.