Crowdsourcing recommendations from social sentiment

In this paper, we investigate an innovative recommendation system by incorporating relevant social opinion and sentiment information. Our recommendation system, a powerful application of social sentiment analysis, differs from many existing models, which investigate the situation where the social network itself is structured to work with the product ranking and is specially built inside an e-commerce website. In contrast, our proposed system focuses on constructing and inferring product recommendations from external social network services (SNS) such as Facebook. In our system, we process product features in a finite-dimensional polynomial linear space. Additional components of our proposed system include an asymmetric similarity measurement and an asymmetric advantage measurement. We also show that our definitions for the two measurements include specific properties that reduce the computational overhead in the experiments. An important aspect of our modeling is to incorporate user-generated high-level semantic sentiment in the analysis. We apply our models to real time data and observe promising results for not only product recommendation but also job recommendation.