SEMO: A Framework for Customer Social Networks Analysis Based on Semantics

Abstract

The increasing importance of the Internet in most domains has brought about a paradigm change in consumer relations. The influence of Social Networks has entered the Customer Relationship Management domain under the coined term CRM 2.0. In this context, the need to understand and classify the interactions of customers by means of new platforms has emerged as a challenge for both researchers and professionals world-wide. This is the perfect scenario for the use of SEMO, a platform for Customer Social Networks Analysis based on Semantics and emotion mining. The platform benefits from both semantic annotation and classification and text analysis, relying on techniques from the Natural Language Processing domain. The results of the evaluation of the experimental implementation of SEMO reveal a promising and viable platform from a technical perspective.

Unraveling Platform Strategies: A Review from an Organizational Ambidexterity Perspective

“…As well, future research may look into the co-evolution of multiple roles and relationships and their impacts on the decision making of platform owners. One relevant platform phenomenon is the effort that owners make to convert demand-side users into supply-side users, and future research may consider how best to facilitate users' participation, engagement, and innovation in a platform ecosystem with evolving roles [117]. Specifically, researchers may study the barriers around and facilitators for creating and maintaining trust in various online transactions [118].…”

ReSySTER: A hybrid recommender system for Scrum team roles based on fuzzy and rough sets

“…Table 11 shows the precision results of three recommender systems developed by the authors. Although the domains of these recommender systems are heterogeneous, they share the subjectivity of the expert decisions (Garcia-Crespo et al, 2011a;2011b) and the classification (Garcia-Crespo et al, 2010). Sem-fit is a recommender system for the touristic information.…”

“…Also the inclusion of a new type of recommendations, such us balanced teams, and new metrics such us mean average precision and mean reciprocal rank, could be performed to improve the proposed system. (Garcia-Crespo et al, 2011b) 0.320 SEMFIT (Garcia-Crespo et al, 2011a) 0.580 SEMO (Garcia-Crespo et al, 2010) 0 …”

“…Meanwhile, SINVLIO (Garcia-Crespo et al, 2011b) is a tool based on semantic technologies and fuzzy logic techniques that recommends investments grounded in both psychological aspects of the investor and traditional financial parameters of the investments. Finally, SEMO (Garcia-Crespo et al, 2010) is a platform for customer social networks analysis based on semantics and emotion mining. The platform benefits from both semantic annotation and classification and text analysis, relying on techniques from the natural language processing domain.…”

PB-ADVISOR: A private banking multi-investment portfolio advisor

“…The use of Precision and Recall metrics for analyzing the effectiveness of recommender systems has been proven by several authors in different domains [26].…”