Game-based crowdsourcing to support collaborative customization of the definition of sustainability

Respective to our data set, SVM outperformed other algorithms. Semantic analysis was insensitive to the depth/number of linguistic features considered. In contrast, sentiment analysis was enhanced when part of speech (PoS) was tracked. Interestingly, our work shows that considering the topic (semantic) of a tweet helped enhance the accuracy of sentiment analysis: including topical class as a feature in conducting sentiment analysis results in higher accuracies. This could be used as means to detect the evolution of community opinion: that topic-based social networks are evolving within the communities tweeting about urban projects. It could also be used to identify the topics of top priority to the community or the ones that have the widest spread of views. In our case, these were mainly the impacts of the design and engineering features on social issues.