Opinion leaders’ prediction for monitoring the product reputation

• Published in: International journal of Web information systems 2018-12, Vol.14 (4), p.524-544
• Main Authors: Ennaji, Fatima Zohra, El Fazziki, Abdelaziz, El Alaoui El Abdallaoui, Hasna, Benslimane, Djamal, Sadgal, Mohamed
• Format: Article
• Language: English
• Subjects: Data mining; Digital media; Leadership; Marketing; Matching; Mismanagement; Product development; Reputations; Sentiment analysis; Social networks; Spamming
• ISSN: 1744-0084; 1744-0092
• DOI: 10.1108/IJWIS-03-2018-0016

PurposeThis paper aims to detect opinion leaders, who they play a vital role as influencers of their community, which will help companies to improve their image in social media. This idea came with the fast development of social media, where individuals are increasingly sharing their personal experiences, opinions and critiques about products through these platforms. Thus, the new customers can rely on these spontaneous recommendations to proceed with the purchase without risk of disappointment. Therefore, the mismanagement of the e-reputation can cause huge losses for companies.Design/methodology/approachIn this study, a product reputation framework based on the prediction of opinion leaders is presented. To do so, opinion mining has been used to determine the product reputation in social media. In addition to posts processing, the profile information has also exploited to predict opinion leaders. To achieve the authors’ goal, spammers and duplicated profiles have been detected to improve the product reputation results.FindingsThe effectiveness of this approach has been tested using a social media simulation. The obtained results show that this approach is efficient and more accurate compared to the classical solutions.Originality/valueThe key novelty is the gathering of spammer detection criteria with different weights and the profiles matching by providing the suitable matching methods that take into account the profile’s attributes types. Consequently, a different similarity measure was assigned for each of the considered four attributes types. These two steps can ensure that the results obtained from social media are actually supported by opinions extracted directly from the real physical consumers.