Visual sentiment prediction with transfer learning and big data analytics for smart cities
Kaoutar Ben Ahmed, Mohammed Bouhorma, Mohamed Ben Ahmed, Atanas Radenski · 2016
Internet of things and social media platforms have changed the way people communicate and express themselves. People are now sharing their experiences and views in blogs, micro-blogs, comments, photos, videos, and other postings in social sites. It has been recognized that timely reactions to public opinions and sentiments and its proper use by city governments are of paramount importance to their missions. Despite of the widely recognized potential of sentiment analysis, relatively little is known about how to best harness its potential benefits for smart cities. The objective of this article is to help fill the void by reviewing the state of the art and opportunities of data sources and applications of sentiment analysis for smart cities. Additionally, The article explores deep features of photos shared by users in Twitter via transfer learning. Thus revealing interesting research opportunities and applications.