Deep learning based analysis of sentiment dynamics in online cancer community forums: An experience

Abstract

Online health communities (OHC) provide various opportunities for patients with chronic or life-threatening illnesses, especially for cancer patients and survivors. A better understanding of the sentiment dynamics of patients in OHCs can help in the precise formulation of the needs during their treatment. The current study investigated the sentiment dynamics in patients’ narratives in a Breast Cancer community group (Breastcancer.org) to identify the changes in emotions, thoughts, stress, and coping mechanisms while undergoing treatment options, particularly chemotherapy, radiation, and surgery. Sentiment dynamics of users’ posts was performed using a deep learning model. A sentiment change analysis was performed to measure change in the satisfaction level of the users. The deep learning model BiLSTM with sentiment embedding features provided a better F1-score of 91.9%. Sentiment dynamics can assess the difference in satisfaction level the users acquire by interacting with other users in the forum. A comparison of the proposed model with existing models revealed the effectiveness of this methodology.