Election Exit Poll Prediction

Predicting the ultimate outcomes of certain municipal elections is still difficult. This app introduces a technique focused on machine learning for analyzing data to predict the ultimate outcomes of many local elections. The proliferation of social media in the recent past has provided end users a powerful platform to voice their opinions. Businesses  need to identify the polarity of these opinions in order to understand user orientation and thereby make smarter decisions. One such application is in the field of politics, where political entities need to understand public opinion and thus determine their campaigning strategy.

Sentiment analysis on social media data has been seen by many as an effective tool to monitor user preferences and inclination. Popular text classification algorithms like Naive Bayes and SVM are Supervised Learning Algorithms which require a training data set to perform Sentiment analysis. The accuracy of these algorithms is contingent upon the quantity as well as the quality of the labelled training data. Since most applications suffer from lack of training data, they resort to cross domain sentiment analysis which misses out on features relevant to the target data. This, in turn, takes a toll on the overall accuracy of text classification.

 Keywords: Election Poll, Prediction, Mood Sensing, Naïve Bayes, SVM.