Knowledge based Techniques for Pragmatic Feature Engineering and Opinion Mining on Divergent Data sets

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Annie Syrien, M. Hanumanthappa, Ravi Kumar


Sentiment Analysis and Opinion Mining is a computational text processing technique used for extraction of emotion from the given text, for e.g. joy, sad, disgust, fear, anger which can be polarized as positive, negative and neutral. The advancement in technology and data augmentation has been an easy access for data collection for the research work. In this research paper, different machine learning algorithms such as Extreme Gradient Boost classifier, Extra tree classifier, K-Nearest Neighbour classifier and Long Short Term Memory deep learning model was computed to classify the divergent datasets such as Twitter Bengaluru traffic data, movie review, fake news classification and financial sentiments. Performance of these classifier was tabulated with respect to independent datasets in terms of accuracy, f1-score, precision, sensitivity, mean squared error and, log loss and graphically represented with the help of PyCharm. The contribution in this paper is a methodology that automates the sentiment analysis of the divergent dataset with context of apprehension of tweets.

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