Contextual polarity and influence mining in online social networks

Abstract

Crowdsourcing is an emerging tool for collaboration and innovation platforms. Recently, crowdsourcing platforms have become a vital tool for firms to generate new ideas, especially large firms such as Dell, Microsoft, and Starbucks, Crowdsourcing provides firms with multiple advantages, notably, rapid solutions, cost savings, and a variety of novel ideas that represent the diversity inherent within a crowd. The literature on crowdsourcing is limited to empirical evidence of the advantage of crowdsourcing for businesses as an innovation strategy. In this study, Starbucks’ crowdsourcing platform, Ideas Starbucks, is examined, with three objectives: first, to determine crowdsourcing participants’ perception of the company by crowdsourcing participants when generating ideas on the platform. The second objective is to map users into a community structure to identify those more likely to produce ideas; the most promising users are grouped into the communities more likely to generate the best ideas. The third is to study the relationship between the users’ ideas’ sentiment scores and the frequency of discussions among crowdsourcing users. The results indicate that sentiment and emotion scores can be used to visualize the social interaction narrative over time. They also suggest that the fast greedy algorithm is the one best suited for community structure with a modularity on agreeable ideas of 0.53 and 8 significant communities using sentiment scores as edge weights. For disagreeable ideas, the modularity is 0.47 with 8 significant communities without edge weights. There is also a statistically significant quadratic relationship between the sentiments scores and the number of conversations between users.