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Article

Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum

Department of Future Convergence Technology, Soonchunhyang University, Asan-si 31538, Korea
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Appl. Sci. 2019, 9(6), 1249; https://doi.org/10.3390/app9061249
Received: 15 February 2019 / Revised: 11 March 2019 / Accepted: 19 March 2019 / Published: 25 March 2019
(This article belongs to the Special Issue Sentiment Analysis for Social Media)
Sentiment analysis is the most common text classification tool that analyzes incoming messages and tells whether the underlying sentiment is positive, negative, or neutral. We can use this technique to understand people by gender, especially people who are suffering from a sensitive disease. People use health-related web forums to easily access health information written by and for non-experts and also to get comfort from people who are in a similar situation. The government operates medical web forums to provide medical information, manage patients’ needs and feelings, and boost information-sharing among patients. If we can classify people’s emotional or information needs by gender, age, or location, it is possible to establish a detailed health policy specialized into patient segments. However, people with sensitive illness such as AIDS tend to hide their information. Especially, in the case of sexually transmitted AIDS, we can detect problems and needs according to gender. In this work, we present a gender detection model using sentiment analysis and machine learning including deep learning. Through the experiment, we found that sentiment features generate low accuracy. However, senti-words give better results with SVM. Overall, traditional machine learning algorithms have a high misclassification rate for the female category. The deep learning algorithm overcomes this drawback with over 90% accuracy. View Full-Text
Keywords: sentiment analysis; gender classification; machine learning; deep learning; medical web forum sentiment analysis; gender classification; machine learning; deep learning; medical web forum
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MDPI and ACS Style

Park, S.; Woo, J. Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum. Appl. Sci. 2019, 9, 1249. https://doi.org/10.3390/app9061249

AMA Style

Park S, Woo J. Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum. Applied Sciences. 2019; 9(6):1249. https://doi.org/10.3390/app9061249

Chicago/Turabian Style

Park, Sunghee; Woo, Jiyoung. 2019. "Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum" Appl. Sci. 9, no. 6: 1249. https://doi.org/10.3390/app9061249

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