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Published on: October 24, 2017
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Early-stage pregnancy recognition on microblogs: Machine learning and lexicon-based approaches
Samer Muthana Sarsam1, Ahmed Ibrahim Alzahrani2, Hosam Al-Samarraie3,4
1School of Strategy and Leadership, Coventry University, Coventry, United Kingdom.
Heliyon
|October 9, 2023
Summary
This study introduces an emotion-based system to detect early pregnancy using Twitter data. Analyzing user sentiments and emotions can help identify potential pregnancies on social media platforms.
Area of Science:
- Computational Social Science
- Digital Health
- Natural Language Processing
Background:
- Pregnancy entails significant medical and psychosocial risks, necessitating proactive health monitoring.
- Early detection of pregnancy is crucial for timely intervention and improved maternal health outcomes.
- Social media platforms offer a rich source of real-time user-generated data for health-related insights.
Purpose of the Study:
- To propose and evaluate an emotion-based mechanism for early-stage pregnancy detection using Twitter data.
- To analyze pregnancy-related emotions and sentiments expressed in social media posts.
- To explore the utility of microblogging sentiments for recognizing early pregnancy.
Main Methods:
- Real-time Twitter data was collected and processed to extract pregnancy-related emotions (anger, fear, sadness, joy, surprise) and sentiment polarity.
- NRC Affect Intensity Lexicon and SentiStrength were employed for emotion and sentiment analysis.
- Part-of-speech tagging and association rules mining were used to map pregnancy terms with sentiments.
Main Results:
- Tweets related to pregnancy exhibited high positivity, with significant expressions of joy, sadness, and fear.
- The analysis revealed distinct emotional and sentiment patterns associated with early pregnancy discussions.
- Classification models demonstrated the feasibility of using social media sentiments for early pregnancy recognition.
Conclusions:
- Emotion and sentiment analysis of social media data can facilitate early pregnancy detection.
- This approach provides valuable, real-time insights for healthcare decision-makers regarding public health trends.
- Leveraging social media data offers a novel avenue for understanding user health status and needs.
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