Research Article
Transgender Community Sentiment Analysis from Social Media Data: a Natural Language Processing Approach
@INPROCEEDINGS{10.4108/eai.13-10-2023.2341032, author={Yuqiao Liu}, title={Transgender Community Sentiment Analysis from Social Media Data: a Natural Language Processing Approach}, proceedings={Proceedings of the 3rd International Conference on New Media Development and Modernized Education, NMDME 2023, October 13--15, 2023, Xi’an, China}, publisher={EAI}, proceedings_a={NMDME}, year={2024}, month={1}, keywords={social media data a natural language processing approach computer science transgender studies data analysis media integration and intelligent communication}, doi={10.4108/eai.13-10-2023.2341032} }- Yuqiao Liu
Year: 2024
Transgender Community Sentiment Analysis from Social Media Data: a Natural Language Processing Approach
NMDME
EAI
DOI: 10.4108/eai.13-10-2023.2341032
Abstract
There are huge disparities in mental health outcomes among transgender people compared to the general population. Interpreting social media data posted by transgender people may help people better understand the emotions of these sexual minorities and intervene early. In this study, 300 social media comments posted by trans people were manually classified into negative, positive and neutral sentiment. Using 5 machine learning algorithms and 2 deep neural networks to build a sentiment analysis classifier based on labeled data. The results show that the annotations are reliable, with Cohen's Kappa scores exceeding 0.8 in all three categories. The LSTM model yielded the best accuracy over 0.85 and an AUC of 0.876. Next steps will focus on using advanced natural language processing algorithms on larger annotated datasets.
