Scalable intelligent data-driven decision making for cognitive cities

Advancement of Web 2.0 has completely revolutionized the way people communicate and exchange information among them using social media such as Twitter etc. People are using smart technology based solutions (including IoT’s, sensors etc.) at an unprecedented scale for enhancing their quality of the life and building a smart city. These social sensors etc. are huge source of sentiment-rich data that needs to be processed and comprehended well in order to facilitate enhanced and smart decision making by the citizens of the smart city for any product, service or policy etc. The objective of this research is to leverage deeper insights of application of intelligent computation techniques for sentiment analysis of cognitive cities using user-generated data to improve the urban ecosystem. Sentiment analysis is quite imperative and serve as info-foundation for smart cities as they have the ability to harness the opinions or sentiments accurately based on the computation technology applied. The results are evaluated and analyzed on Twitter datasets that are constructed by the students of the Stanford University. It is observed that the alliance of big data and social media analytics using intelligent sentiment computation has helped in making citizens smart for taking smart decisions and eventually building a smart city.