出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Intelligent Models for predicting diseases whether building a model to help the doctor or evenpreventing its spread in an area globally, is increasing day by day. Here we present a nobleapproach to predict the disease prone area using the power of Text Analysis and MachineLearning. Epidemic Search model using the power of the social network data analysis and thenusing this data to provide a probability score of the spread and to analyse the areas whethergoing to suffer from any epidemic spread-out, is the main focus of this work. We have tried toanalyse and showcase how the model with different kinds of pre-processing and algorithmspredict the output. We have used the combination of words-n grams, word embeddings and TFIDFwith different data mining and deep learning algorithms like SVM, Naïve Bayes and RNNLSTM.Naïve Bayes with TF-IDF performed better in comparison to others.
关键词：Natural Language Processing; Text Mining; Text Analysis; Support Vector Machines; LSTM;Naive Bayes; TextBlob; Tweet Sentiment Analysis
