Nimai Chand Das Adhikari, Vamshi Kumar Kurva, Suhas S, Jitendra Kumar Kushwaha, Ashish Kumar Nayak, Sankalp Kumar Nayak and Vaisakh Shaj, Indian Institute of Space Science and Technology - Thiruvananthapuram, India

Intelligent Models for predicting diseases whether building a model to help the doctor or even preventing its spread in an area globally, is increasing day by day. Here we present a noble approach to predict the disease prone area using the power of Text Analysis and Machine Learning. Epidemic Search model using the power of the social network data analysis and then using this data to provide a probability score of the spread and to analyse the areas whether going to suffer from any epidemic spread-out, is the main focus of this work. We have tried to analyse and showcase how the model with different kinds of pre-processing and algorithms predict the output. We have used the combination of words-n grams, word embeddings and TF-IDF with different data mining and deep learning algorithms like SVM, Naïve Bayes and RNN-LSTM. Naïve Bayes with TF-IDF performed better in comparison to others.

Natural Language Processing, Text Mining, Text Analysis, Support Vector Machines, LSTM, Naive Bayes, TextBlob, Tweet Sentiment Analysis