A Novel Approach to Big Data Veracity using Crowdsourcing Techniques and Bayesian Predictors

In today's world data is being generated at a tremendous pace and there have to be enough measures in place to verify the nature of big data. Analysis performed on 'dirty' data may lead to erroneous insights and thereby shaping decisions poorly. The aspect of big data that deals with its correctness is known as big data veracity. Trusting the data acquired goes a long way in implementing decisions from an automated decision-making system and veracity helps to validate the data acquired. In this paper, we present our solution to the big data veracity problem using crowdsourcing techniques. Our solution involves the use of sentiment analysis, which deals with identifying the sentiment expressed in a piece of text. As a proof of concept, we have developed an app that requires users to tag tweets as per the sentiment it evokes in them. Each tweet would therefore get ratified by hundreds of our participants and the sentiment associated to the tweet gets tagged. The tagged emotion was then evaluated against the verified emotion as compared to a verified data set. This analysis was then plotted on a ROC curve and also evaluated against verified data using a Bayesian predictor trained with a trinomial function. As can be seen, an accuracy of 81% was obtained as displayed by the ROC curve and 89% through the Bayesian predictor. Also, a MAP analysis of the Bayesian predictor yields neutral sentiment as the most probable hypothesis. By doing this, we have proven that crowdsourcing of sentiment analysis is a viable solution to the problem of big data veracity and therefore an aid in making better decisions.