Transgender community is experiencing a huge disparity in mental health conditions compared with the general population. Interpreting the social medial data posted by transgender people may help us understand the sentiments of these sexual minority groups better and apply early interventions. In this study, we manually categorize 300 social media comments posted by transgender people to the sentiment of negative, positive, and neutral. 5 machine learning algorithms and 2 deep neural networks are adopted to build sentiment analysis classifiers based on the annotated data. Results show that our annotations are reliable with a high Cohen's Kappa score over 0.8 across all three classes. LSTM model yields an optimal performance of accuracy over 0.85 and AUC of 0.876. Our next step will focus on using advanced natural language processing algorithms on a larger annotated dataset.

Tinder is attempting to get more in step with the transgender community. Speaking at the Code Conference on Thursday, Tinder CEO Sean Rad revealed that the company is about to roll out new features in the popular dating app that will account for gender besides male and female, while he couldn't yet get into specifics. For users who are transgender, Tinder has been tricky -- if not overtly dangerous and risky -- to navigate in its current form. Trans users have been banned from the app after being reported by non-trans users who match with them. Others trans users report experiencing harassment on the app, or even fearing meeting up with users who might be unaware of or hostile to their gender status. For banned users, Tinder has historically reviewed and reinstated accounts that did not violate community guidelines.