JMIR Publications

ABSTRACT

Background:

Complementary therapies are being increasingly used by cancer survivors. As a channel for customers to share their feelings, outcomes, ideas, and perceived knowledge about the products purchased from e-commerce platforms, Amazon online reviews are a valuable real-world data source for understanding cancer survivorship care needs.

Objective:

In this study, we aim to highlight the potential of using Amazon consumer reviews as a novel source for identifying cancer survivorship care needs, particularly related to symptom self-management. Specifically, we 1) present a publicly available, manually annotated corpus derived from Amazon reviews of health-related products, and 2) develop baseline NLP models using deep learning and large language model (LLM) approaches to demonstrate the usability of this dataset.

Methods:

We preprocessed the Amazon review dataset to identify sentences with cancer mentions through a rule-based method, and conducted content analysis including text feature analysis, sentiment analysis, topic modeling, cancer type and symptom association analysis. We then designed an annotation guideline, targeting survivorship-relevant constructs. A total of 159 reviews were annotated, and baseline models were developed based on deep learning and large language model (LLM) for name entity recognition and text classification tasks.

Results:

A total of 4,703 sentences containing positive cancer mentions were identified, drawn from 3,349 reviews associated with 2,589 distinct products. The identified topics through topic modeling revealed meaningful insights into cancer symptom management and survivorship experiences. Examples included discussions of green tea use during chemotherapy, cancer prevention strategies, product recommendations for breast cancer. Top 15 symptoms in reviews were also identified, with pain being the most frequent symptom, followed by inflammation, fatigue, hot flashes, dry mouth, constipation, etc. The annotation labels were designed to capture cancer types, indicated symptoms, and symptom management outcomes. The resulting annotation corpus contains 2,067 labels from 159 Amazon reviews. It’s publicly accessible, together with the annotation guideline through the Open Health Natural Language Processing (OHNLP) Github. Our baseline model, bert-base-cased, achieved highest weighted average F1, i.e., 66.92%, for NER, and LLM gpt4-1106-preview-chat achieved the highest F1 for text classification tasks, i.e., 66.67% for “Harmful outcome”, 88.46% for “Favorable outcome” and 73.33% for “Ambiguous outcome”.

Conclusions:

Our results demonstrate the potential of Amazon consumer reviews as a novel data source for identifying persistent symptoms, concerns, and self-management strategies among cancer survivors. This corpus, along with the baseline NLP models developed for named entity recognition and text classification, lays the groundwork for future methodological advancements in cancer survivorship research. Importantly, insights derived from this study could be evaluated in relations to established clinical guidelines for symptom management in cancer survivorship care. By revealing the feasibility of using consumer-generated data for mining survivorship-related experiences, this study offers a promising foundation for future research and argumentation analysis aimed at improving long-term outcomes and support for cancer survivors.