Emerging Technologies for Developing Countries. First International EAI Conference, AFRICATEK 2017, Marrakech, Morocco, March 27-28, 2017 Proceedings

Research Article

Opinions Sandbox: Turning Emotions on Topics into Actionable Analytics

  • @INPROCEEDINGS{10.1007/978-3-319-67837-5_11,
        author={Feras Al-Obeidat and Eleanna Kafeza and Bruce Spencer},
        title={Opinions Sandbox: Turning Emotions on Topics into Actionable Analytics},
        proceedings={Emerging Technologies for Developing Countries. First International EAI Conference, AFRICATEK 2017, Marrakech, Morocco, March 27-28, 2017 Proceedings},
        proceedings_a={AFRICATEK},
        year={2017},
        month={10},
        keywords={Social commerce Opinion extraction Topic extraction Actionable analytics},
        doi={10.1007/978-3-319-67837-5_11}
    }
    
  • Feras Al-Obeidat
    Eleanna Kafeza
    Bruce Spencer
    Year: 2017
    Opinions Sandbox: Turning Emotions on Topics into Actionable Analytics
    AFRICATEK
    Springer
    DOI: 10.1007/978-3-319-67837-5_11
Feras Al-Obeidat1, Eleanna Kafeza1, Bruce Spencer2,*
  • 1: Zayed University
  • 2: University of New Brunswick
*Contact email: bspencer@unb.ca

Abstract

The Opinions Sandbox is a running prototype that accesses comments collected from customers of a particular product or service, and calculates the overall sentiment toward that product or service. It performs topic extraction, displays the comments partitioned into topics, and presents a sentiment for each topic. This helps to quickly digest customers’ opinions, particularly negative ones, and sort them by the concerns expressed by the customers. These topics are now considered issues to be addressed. The Opinions Sandbox does two things with this list of issues. First, it simulates the social network of the future, after rectifying each issue. Comments with positive sentiment regarding this rectified issues are synthesized, they are injected into the comment corpus, and the effect on overall sentiment is produced. Second, it helps the user create a plan for addressing the issues identified in the comments. It uses the quantitative improvement of sentiment, calculated by the simulation in the first part, and it uses user-supplied cost estimates of the effort required to rectify each issue. Sets of possible actions are enumerated and analysed showing both the costs and the benefits. By balancing these benefits against these costs, it recommends actions that optimize the cost/benefit tradeoff.