Exploiting social media data by extracting key information from it is one of the great challenges in data mining and knowledge discovery. Every election campaign has an online presence of voters which uses these social media platform to express their sentiments and opinions towards political parties, leaders and important topics. This paper presents a novel data collection technique for prediction of election outcomes and a topic modeling method for extracting topics. Data collection technique used RSS (Rich Site Summary) feeds of news articles and trending keywords from Twitter simultaneously and constructed an intelligent prediction model based primarily on the volume of tweets and sentiment of users. This paper effort to improve electoral predictions using social media data based dynamic keyword methodology.
Different techniques for electoral prediction based on social media data has been investigated based on existing literature and isolate the factors which improve our methodology. Meaningful inferences such as the popularity of leaders and parties during different intervals, trending issues, and important factors are extracted from the data set. The election outcomes are compared with traditional methods used by survey agencies for exit polls and validation of results showed that social media data can predict with better accuracy. The research has identified that data collection technique and timing play an important role in yielding better accuracy in predicting outcomes and extracting meaningful inferences.