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Adverse Drug Reaction Related Post Detecting Using Sentiment Feature
Jingfang Liu1, Xiaoyan Jiang1, Qiangyuan Chen2
1Dept. of Information Management, School of Management, Shanghai University, Shanghai 200444, China.
Iranian Journal of Public Health
|August 9, 2018
Summary
This study introduces an effective method for detecting Adverse Drug Reaction (ADR) posts on social media. By utilizing sentiment features and chi-squared (CHI) statistics, the approach enhances post detection accuracy for improved drug surveillance.
Area of Science:
- Social Media Analysis
- Pharmacovigilance
- Natural Language Processing
Background:
- Social media posts offer valuable data for post-marketing drug surveillance.
- Detecting Adverse Drug Reaction (ADR) posts requires effective methods beyond basic domain features.
Purpose of the Study:
- To develop a more effective method for detecting ADR-related posts using social media data.
- To evaluate the impact of sentiment features and feature selection techniques on ADR post detection.
Main Methods:
- Collected and manually annotated 1814 diabetes-related social media posts from Shanghai in 2016.
- Generated sentiment features and employed chi-squared (CHI) statistics for feature selection.
- Evaluated detection performance using different feature sets, including sentiment and CHI-selected features.
Main Results:
- Sentiment features, selected using CHI statistics, significantly improved ADR-related post detection.
- The method demonstrated superior performance in identifying ADR-related posts compared to non-ADR posts.
- Combining sentiment features with CHI feature selection yielded the highest detection performance, proving the method's effectiveness.
Conclusions:
- The integration of sentiment features and CHI feature selection provides an effective strategy for detecting ADR-related social media posts.
- This approach enhances the utility of social media data for post-marketing drug safety surveillance.
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