A Review on Data Driven Machine Learning Models for Semantic and Emotion Identification

Sentiment analysis has a wide range of applications in information systems, including classifying reviews, summarizing review and other real time applications. There are promising possibilities to use sentiment analysis in real time business models. The present work focuses on sentient analysis by classification of tweets from social media (twitter) data. In this case, a deep Bayes net has been employed for training and subsequent testing of tweets. The Bayesian Regularization (BR) algorithm has been used and their respective results show variation in outcomes because of its probabilistic classification. It has been shown that the proposed algorithm attains a classification accuracy of 99.3% which is substantially higher than previously existing method.

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