Mining in social media data : happiness forecast @ SG - Tan, Poh Lian (2018)
Individual happiness in each region play a significant role for social metric. Happiness has often indirectly characterized and overshadowed by social media indicators. This project studies a methodology to measure the correlation between the real time expressions of individuals made across Singapore and range of social phenomena factors- population, dengue cluster and electricity consumption. We will examine the expression made on the social media -Twitter and uncover the happiness index over different regions. A total of 10,000 raw data in Twitter was collected which consists of users share thoughts, images, links for all the regions in Singapore. The collection of real-time tweets is customised to suit our project by using streaming API in Python. The next stage is to perform text-mining techniques to obtain the meaningful term. After data cleaning and pre-processing phrase, the parsed term will be tagged to a happiness index dictionary to compute the happiness scores (H-Score). Additionally, happiness index of singlish tokens will be further classified with Sentic API. Finally, we will be evaluating the relationships between the happiness scores and the real-world phenomena.