GSOR09 Presentation Time: 5:40 PM

Abstract

Purpose

The utilization of artificial intelligence in analyzing patient discussions on online platforms can uncover valuable experiential data that are often overlooked in structured surveys. Sentiment analysis, a branch of natural language processing (NLP), interprets and classifies emotions within text, offering insights into patient sentiments as positive, negative, or neutral. This study aimed to apply AI techniques to analyze the sentiments of posts on a cervical cancer-related online forum, specifically focusing on discussions related to brachytherapy.

Materials/Methods

Utilizing a Reddit Application Programing Interface, we extracted posts and comments from the subreddit r/cervicalcancer, focusing on discussions about brachytherapy between November 2020 and January 2024. We then processed the data in multiple steps including cleaning, lowercasing, removing illegible text, and tokenization. We analyzed the entries using RoBERTa (Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach), a sophisticated pre-trained deep learning model, to refine and categorize sentiments. The model assessed the probabilities of the posts being positive, negative, or neutral. We further evaluated and categorized posts using pre-defined keyword tagging to uncover dominant topics within the conversations. These topics were modeled based on recently published literature related to the experiences of patients undergoing cervical brachytherapy.

Results

The analysis encompassed 879 out of 1,073 unique textual entries. Of these, overall sentiments were categorized as 40.1% positive, 30.1% negative, and 29.8% neutral. A specific focus on 'Bowel Domain’ discussions revealed a predominance of negative sentiments (51.2%)—the highest across all topics. Similarly, 'Urinary Domain' (46.8%), 'Fatigue' (42.4%), 'Anesthesia' (41.4%), and 'Pain' (43.4%) discussions largely reflected negative sentiments. In contrast, 'Physical Therapy' and 'Survivorship' discussions were predominantly positive, with 51.2% and 45.5% of posts, respectively. The sentiments on 'Sex' and 'Mental Health' related topics displayed a more balanced distribution between positive and negative perspectives.

Conclusion

This study demonstrates the value of using advanced AI models, such as sentiment analysis, to easily understand online patient discussions. These tools can bridge the gap between clinical insights and patient experiences, enhancing the feedback loop into clinical decisions, consent discussions, and patient education. Further research into the use of such models is necessary to fully leverage the insights they provide.