Social Data Sentiment Analysis in Smart Environments - Extending Dual Polarities for Crowd Pulse Capturing

Social networks drive todays opinion and content diffusion. Humans interact in social media on the basis of their emotional states and it is important to capture people emotional scales for a particular theme. Such interactions are facilitated and become evident in smart environments characterized by mobile devices and new smart city contexts. This work proposes a sentiment analysis approach which extends positive and negative polarity in higher and wider emotional scales to offer new smart services over mobile devices. A particular methodology and a generic framework is outlined along with indicative mobile applications which employs microblogging data analysis for chosen topics, locations and time. These applications capture crowd pulse as expressed in microblogging platforms and such an analysis is beneficial for various communities such as policy makers, authorities and the public.