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Understanding Cancer Survivorship Care Needs Using Amazon Reviews: Content Analysis, Algorithm Development, and

Liwei Wang¹, Qiuhao Lu², Rui Li²

¹Department of Clinical and Health Informatics, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, 7000 Fannin Street, Suite 600, Houston, TX, 77030, United States, 1 713-500-3900.

|September 23, 2025

Summary

This summary is machine-generated.

Amazon reviews offer valuable insights into cancer survivorship care needs, particularly for symptom self-management. This study presents a novel dataset and baseline models to analyze these real-world data for improved patient support.

Keywords:

annotation baseline models cancer research cancer survivorship care deep learning large language model natural language processing real-world data

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Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

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Published on: September 27, 2024

Area of Science:

Computational linguistics
Health informatics
Oncology

Background:

Cancer survivors increasingly use complementary therapies.
Amazon reviews serve as a rich source of real-world data on patient experiences and needs.
Understanding survivorship care needs is crucial for improving patient outcomes.

Purpose of the Study:

To explore the potential of Amazon consumer reviews for identifying cancer survivorship care needs.
To develop a manually annotated corpus from Amazon reviews for research.
To establish baseline natural language processing (NLP) models for analyzing this data.

Main Methods:

Preprocessing Amazon reviews to identify cancer-related sentences.
Conducting content analysis, including topic modeling and sentiment analysis.
Developing annotation guidelines and creating a corpus for named entity recognition and text classification.

Main Results:

Identified 4703 sentences from 3349 reviews related to cancer.
Topic modeling revealed insights into symptom management and survivorship experiences.
Baseline models achieved F1-scores up to 66.92% for NER and 88.46% for text classification.

Conclusions:

Amazon reviews are a viable data source for understanding cancer survivor needs and self-management strategies.
The created corpus and baseline models support future research in cancer survivorship.
This approach can inform clinical guidelines and improve support for cancer survivors.