Abstract
Women’s lives change with pregnancy and birth. Pregnancy and delivery, like other fleeting life events, can have lasting psychological and physiological effects on women. They also increase Postpartum Depression (PPD) rates. If untreated, PPD can drain a mother’s energy and make it hard to care for her children. Mental diseases are multifaceted, making social media identification difficult. Social media like Instagram, Twitter, Facebook, and others have grown in importance, changing this study topic. Mothers can communicate to their friends and share their feelings, images, and videos on social media. Posts let us see how freshly delivered mothers talk to each other on social networks. This study identifies postpartum depressive women from Twitter messages that show depressed attitudes. Thus, we use NLP for prediction and machine learning to evaluate our proposed approach. We list the most common words used by PPD and control group account holders here. Our strategy improves performance using integrated attribute extraction. Bigram best detects PPD with 82% accuracy and 0.80 F1 scores using the Random Forest (RF) classifier and single attribute extraction. SVM classifiers perform best for PPD detection, with 89% accuracy and 0.91 F1 scores. Our research reveals that effective attribute selections and their many attribute combinations can boost performance.
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Gopalakrishnan, A., Gururajan, R., Venkataraman, R., Zhou, X., Chan, K.C. (2023). A Combined Attribute Extraction Method for Detecting Postpartum Depression Using Social Media. In: Li, Y., Huang, Z., Sharma, M., Chen, L., Zhou, R. (eds) Health Information Science. HIS 2023. Lecture Notes in Computer Science, vol 14305. Springer, Singapore. https://doi.org/10.1007/978-981-99-7108-4_2
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