Framework for SLEPS : Self learning electoral results prediction system

Abstract

Computational social science is the study that focuses on developing and analyzing computation models for social issues. Elections are major social events that impact society. Behavioral analysis is a factor which affects electoral result prediction. It imbibes geographic comments, sentiment, and psychology of the participants. Deliberation in judgment based on gender, place of birth, native tongue and genealogy affects the results of electoral polls. Study of the impact of any event whether positive or negative that directly or indirectly influence the comments of the people stands as a factor affecting the elections. SLEPS is a proposed framework that uses large heterogeneous data set to train dual layer neural nets to increase the credibility and authenticity of polls. In SLEPS, we map data of any presidential or gubernatorial election comprising of social traits to statistical learning using neural nets. It is “self-learning’’ as it involves importance weighting of experts that incorporates features that affect election prediction viz. facial appearance, deliberate judgment, character traits and background data of the contestant. SLEPS was evaluated over Indian Elections of 1985 and predicted the winner with root mean square error of 0.04 and 76% precision.

Keywords:

• National Elections
• Character Traits
• Participant Response Traits
• Judgement Types
• Contestant Traits
• Dual Neural Network
• Feed Forward Neural Network