# Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum
## 2 Related Works  
In most previous studies on gender classification, the various features fed to machine learning algorithms. At the Islamic Women's political forum, [PERSON] et al. built a machine learning model that classifies genders by properly combining the characteristics of vocabulary, syntax, structure, uni-gram, and bi-grams [6]. In the study of [PERSON] et al. [7], they used logistic regression and SVM (a support vector machine) to estimate the gender, age, and location of Twitter users. [PERSON] et al. [8] suggested research based on the Latent Dirichlet Allocation (LDA) model using text data from Facebook or Twitter, in which women have been shown to use a lot of personal themes while men tend to post a lot of philosophical or informative text. In the study of [PERSON] and [PERSON] [9], they surveyed the emotions of male and female college students and showed that gender can be distinguished by analyzing emotions using Fastcluster. In the study of [PERSON] et al. [10], they presented a naive Bayes classification approach to identify the gender of weblog authors. They used weblog-specific features such as webpage background colors and emoticons. They report an F-measure of around 64% using their features. [PERSON] and [PERSON]. [11] proposed two new techniques. They used their own POS Sequence mining algorithm and an Ensemble feature selection technique, and achieved an accuracy of 88%. [PERSON] and [PERSON] [12] proposed a user classification model applying machine learning algorithm to the feature set including user profile, user tweeting behavior, linguistic content of user messages, and user social network features. They explored the feature importance and found that linguistic features are consistently effective in user classification. [PERSON] et al. [13] present two systems, a manual feature extraction system and a deep learning method, to automatically classify the gender of blog authors. For the deep-learning-based model, they apply a Bidirectional Long Short-Term Memory Network. [PERSON] and [PERSON] [14] report an accuracy of 86% in gender classification on blog datasets by applying deep learning models based on the Windowed Recurrent Convolutional Neural Network (WRCNN). [PERSON] et al. [15] proposed textual meta-attributes, taking into account the characters, syntax, words, structure, and morphology of short, multi-genre, content-free text posted to Twitter to classify an author's gender via three different machine learning algorithms. The novel contribution of this work is to employ a word-based meta-attribute such as Ratio between hapax dislegomena (a word that appears only twice in a whole text) and the total number of words. Furthermore, they developed a textual morphology based on the meta-attributes from textual structures such as the ratio between the number of pronouns and the total number of words. This work achieved 81% accuracy, but the performance by each class is not presented. [PERSON] [16] proposed a statistical approach to the task of gender classification in tweets. The statistical features include skewness, kurtosis, and central moments; these statistical features make the learning algorithm context-free and language-independent. Recently, Convolutional Neural Networks (CNNs) have also been successful in various text classification tasks. [PERSON] [17] showed that a simple CNN with little hyper-parameter tuning and static vectors achieves excellent results. [PERSON] et al. [18] proposed a deep learning model for Twitter sentiment classification. They advanced a CNN model to adjust word embedding using unsupervised learning. Based on previous works, females and males are found to have differences in terms of the vocabulary used and the emotional expression. During a literature review, we found some limitations in the current literature. First, most recent works focus on Twitter or weblogs. Even though the gender information disclosed in the medical web forum, especially on sexually transmitted diseases, is important, we hardly found any work dealing with medical web forums.