Sentiment Analysis for Patient Reviews in Hospitals by CNN and LSTM Neural Networks Using Pretrained Word Embeddings - Altundogan, Turan Goktug; Karakose, Mehmet; Yilmazer, Sumeyye; Hanoglu, Eray; Demirel, Sedef (Institute of Electrical and Electronics Engineers Inc., 2023)
Medical reviews of patients are very important for the medical management departments and sentiment analysis is one of the most popular application areas of Natural Language Processing. In this study, we use and compare different neural architectures for sentiment analysis of patient reviews about hospitals. We developed four neural models to classify the patient review as positive or negative. First, the data retrieved from an online platform were preprocessed. Then, before the neural training, Skipgram word embeddings were carried out for transfer learning. Finally, training was performed. A model which we trained has only fully connected dense layers. One of the trained models includes LSTM and fully connected layers. One of them includes CNN and fully connected layers. One model has CNN, LSTM and fully connected layers. After the training phases our best two neural models (LSTM-CNN and LSTM) have achieved sentiment classification with over 85% performance. © 2023 IEEE.
2023 Innovations in Intelligent Systems and Applications Conference, ASYU 2023 -- 11 October 2023 through 13 October 2023 -- Sivas -- 194153
Keywords: CNN; LSTM; Medical Review Analysis.; Sentiment Analysis