LSTM Based Sentiment Analysis on Conversations in Health

Abstract

Sentiment analysis from health data is to classify texts in the field of health with sentiment analysis. Research analysis on emotion covers as many fields as politics, economics and health. Sentiment analysis from health data will contribute to human-computer interaction, since they perceive emotions in their machines and make them look like human beings. In this article, the existing data in the field of health has been classified by sentiment analysis. For the realization of this sentiment analysis, the deep learning model lstm deep learning model was used. LSTM has been very successful in processing long texts. In this method used, first of all, the data for model training was made ready with text processing methods. Model training was carried out with this data set, which was prepared with the LSTM deep learning model for model training. As a result of this model training, our success score was 94%, and when we compared it with the results of the articles and studies I examined in the literature, we see that our model gave a very good result. When this result is compared with other well-known classifiers, the efficiency of the proposed algorithm is convincingly proven. © 2022 IEEE.