  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.
