# Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum
## 1 Introduction  
Sentiment analysis is contextual mining of text that identifies and extracts subjective information in source material, helping a business to understand the social sentiment of their brand, product, or service while monitoring the online conversation. With recent advances in machine learning, text mining techniques have improved considerably. Creative use of advanced artificial intelligence techniques can be an effective tool for doing in-depth research.  
Sentiment analysis of web content is becoming increasingly important due to augmented communication through Internet sources such as e-mail, websites, forums, and chat rooms. By collecting these articles and analyzing people's emotions expressed in them, we can figure out people's feelings and opinions about polices, products, brands, and so on. Compared to traditional surveys and other research techniques, this rich information can be obtained with less cost and effort. People can search for particular information based on their individual needs. Patients, patients' significant others, or caregivers also use health-related web forums to get health and medical information and to get comfort from people who are similar to themselves. They also ask questionsabout the disease and find information that is easy to understand [1]. The medical forum also reflects their feelings [2]. The government relies on web forums to act as a helpdesk to promote people's well-being. Some governments directly operate medical web forums to provide medical information, get to know patients' needs, manage their emotions, and help people share information [3]. Users divulge some pieces of personal information when they sign up, and this information can be used to establish national health care policies. However, for sensitive disease such as AIDS, patients tend not to expose their personal information. In terms of health policy, it is important to know who is looking for more information. If we can guess the information from the non-disclosed data, we can understand who is suffering from a specific disease and what types of worries they might have. Especially, in sensitive diseases, by understanding patients we can help them deal with their situation properly and adapt to society. The analysis of AIDS patients' communication could be useful from a government perspective, too.  
Categorizing and analyzing according to demographic information such as gender, age, and region is essential to obtain information such as consumers' emotions, values, and attitudes in all areas of marketing. In particular, it is important to distinguish gender for detailed policy establishment because the situation and necessity can be different by gender. It can also help users to see what topics are most talked about by males and females, and what services are liked or disliked by men and women [4]. Knowing this information is crucial for market intelligence because the information can be used in targeted advertising and service improvement.  
Our study is based on a real-life application of a web forum. In this study, we collect data from the AIDS-related bulletin board at Healthboards.com [5], which is one of the top 20 health websites according to Consumer Reports Health Web Watch. Under the premise that \"Emotions expressed by men and women will be different,\" we propose a model that extracts words and emotions from text messages and distinguishes the gender. By establishing a learning model using gender information, we figure out unclassified gender information and identify the gender awareness of AIDS patients by gender.