# Gender Classification Using Sentiment Analysis and Deep Learning in a Health Web Forum
## 5 Discussion and Conclusions  
In this study, we constructed a model to detect gender information by using machine learning algorithms based on word and sentiment feature sets. We developed various sentiment feature sets including the number of emotion types expressed in a message, objectivity, and emotional complexity.  
To check the effectiveness of sentiment features in gender classification, we varied the feature sets, including sentiment, senti-word, sentiment+word feature sets and using machine learning algorithms or deep learning algorithms. From the experiments, we found that sentiment features with a small number of features and domain-independent are not enough for classifying gender in the medical web forum. Words should be incorporated to classify the author's gender. We found that it is possible to construct gender classification models by using a sentiment+word feature set. In cases of a lightweight system with a limited number of features, SVM performed moderately with senti-word features.  
However, overall, the traditional machine learning algorithm failed, having low precision in the female class. Many male authors were misidentified as female authors. When we applied the deep learning algorithm, both gender classes were identified well. The senti-word feature set and word feature set both worked well, with a 2% accuracy gap. The number of senti-word features is 20% of the number of word features, but generates a comparable performance.  
As a design issue, to embed text into a matrix form, we tested word-embedding methods, one random and the other pre-built word2 Vec. To find the better model, we varied the CNN structure by adding and deleting hidden layers, the convolutional layer, and the max pooling layer.  
This work poses remaining challenges as follows. We tested the word2 Vec built by Google and found that it does not work well for our dataset. In future work, we will build our own word2 Vec suitable for medical web forums by adopting Google's training method. A consideration of word similarity would improve the detection performance. Secondly, in the current work, we eliminated stopwords, numbers, and special characters to reduce the feature set. However, previous work has suggested that these words could be helpful in the text classification task. In the deep learning model, feature reduction is not necessary, so we will check their effectiveness. In addition, we intend to add features in the context of social networks by analyzing the relationship between the posts and comments in order to improve the accuracy of the proposed model. The social network features are also generic across disease types and forums.  
[PERSON] performed the experiments and writing. [PERSON] collected the dataset and participated in writing.  
This work was supported by the research fund of Soonchunhyang University (project number 20171094) and a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP; Ministry of Science, ICT & Future Planning (NRF-2017R1D1A3B03036050)).  
The authors gratefully acknowledge the support by the research fund of Soonchunhyang University (project number 20171094) and a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP; Ministry of Science, ICT & Future Planning (NRF-2017R1D1A3B03036050)).  
The authors have no conflicts of interests.