Predicting Indian Sentiments of COVID-19 Using MLP and Adaboost
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Abstract
Deep Learning is a subset of AI, acknowledged as graded learning. It depends on neural networks and undergoes different stages of representative transforms. It is widely used in image processing,market-crate investigation and classifying student’s performance etc. In present times, deep learning is widely used by the researchers to perform COVID data classificationand clustering and further aids in diagnosing COVID. As this deadly virus is spreading rapidly in India and effected life’s of many in various ways, therefore sentiments of public towards this are classified and analyzed. This Research work collected tweets related to impact of COVID-19 in India, sentiments of people towards COVID-19 using twitter API. Preprocessing strategies were imparted to COVID tweets in order to well-ordered tweets dependent on the subject of the tweet whether tweet belongs to the class-positive, negative and neutral.Subsequent to Preprocessing, cleaned tweets and its associated class are prepared, sent for training and a perfect model is achieved, which is further tested by feeding testing data. Outcomes are assessed on few considerations like Accuracy, RMSE, Precision, Recall, F-Score, ROC Curve and Kappa Statistics. It was found that conventional deep learning method-MLP outperforms in classifying the tweets in terms of high accuracy with 97%, low root mean square error-0.12, precision and recall-0.97. The outcomes also specify good F-Score-0.95 and ROC Curve area-0.99.
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