Analyzing Delhi Assembly Election 2015 Using Textual Content of Social Network

With the emergence of web 2.0, a large number of social networking sites (SNSs) have been evolved. Now-a-days, these social networking sites are attracting millions of users for sharing their views on various issues (e.g. politics, sports, products). We believe that, due to active participation of millions of users on social networking sites the SNSs forms a virtual world that has very close correspondence with the real world communities that possess immense possibilities to mirror the real world events and activities. Motivated with this, in this work we performed analysis of the textual content of Twitter's data related to Delhi Assembly Election 2015 in a manner to predict election results. The main contributions of this work includes (i) Preparation and use of events and time specific training dataset to train the classifier for better accuracy (ii) Design of mapping functions that maps the Twitter's sentiment share to the seat counts of top three contesting parties, with minimum root mean squared error (RSME) regardless of having lots of demographic diversities. The overall results are very close to ground reality, which strengthen our beliefs.