Socializing business process using enterprise social network sentiment analysis: ESAF

Sentiment analysis has become a rich research area due to the growth of social networks applications in the enterprise market. The influence of sentiment analysis has entered the business process domain through enterprise social networks. Sentiment analysis collected from public applications such as Twitter helps organizations to improve their business processes in order to provide good service or better products. However, the amount of research in this field is limited. Existing studies and researches focus only on the results of sentiment analysis without considering impact of these results on the organization business process and how it effects the improvement of products or services. In this context, this research identifies the process of reusing the analysis of sentiment analysis in the organization business application and proposes a framework, eSAF (Enterprise Sentiment Analysis Framework) to enhance organization business processes using Twitter sentiment analysis. The framework crawlers Twitter API from ESN, filter gathered data and apply sentiment analysis techniques based on Naïve Bayes algorithm. Finally, it exposes the result into a SOA environment in the form of web services to be used in other business applications. The framework shows promising results in term of users’ opinions and satisfaction, which provides organizations with accurate statistics about their products or services allowing for future improvements.