Transgender Community Sentiment Analysis from Social Media Data: A Natural Language Processing Approach
This addresses mental health monitoring for transgender communities, but is incremental as it applies standard methods to a new dataset.
The study tackled sentiment analysis of social media comments from transgender people to understand their mental health disparities, achieving an LSTM model accuracy over 0.85 and AUC of 0.876.
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.