# Sentiment analysis of cancer screening in Chinese social media: Qualitative studies based on machine learning

## TL;DR

This study explores public sentiments about cancer screening in China using social media data and machine learning to understand perceptions and inform better health communication strategies.

## Contribution

The novel contribution is the use of sentiment analysis on Chinese social media to identify emotional responses and barriers to cancer screening.

## Key findings

- Seven distinct emotional categories were identified in public discussions about cancer screening.
- Negative emotions like fear and stigma were found to hinder screening participation.
- Findings suggest strategies to improve nursing communication and public screening engagement.

## Abstract

Explore public perceptions and sentiments about cancer screening on social media. The dissemination of misinformation and negative attitudes continue to impede the access of many individuals with perceived risk to cancer screening services despite their awareness of the necessity and concept of early cancer screening.

This study was divided into five steps: data collection, data cleaning, data standardization, sentiment analysis, and content analysis.

This study analyzed 796 social media comments (53,151 words) from Weibo, Zhihu, and Xiaohongshu to explore public sentiments toward cancer screening. Seven emotion categories emerged: good, happy, surprise, anger, disgust, fear, and sadness. Positive emotions reflected trust in physicians, financial support, and perceived screening effectiveness, whereas negative emotions reflected fear of cancer, stigma, and procrastination.

The findings of this study include the development of health communication strategies, the promotion of public screening participation, and the improvement of nursing personalization and emotional sensitivity. These findings highlight barriers and facilitators for cancer screening promotion in China and inform targeted nursing communication strategies.