Outcomes of coming out: Analyzing stories of LGBTQ+

Krithika Ramesh, Tanvi Anand


Abstract
The Internet is frequently used as a platform through which opinions and views on various topics can be expressed. One such topic that draws controversial attention is LGBTQ+ rights. This paper attempts to analyze the reaction that members of the LGBTQ+ community face when they reveal their gender or sexuality, or in other words, when they ‘come out of the closet’. We aim to classify the experiences shared by them as positive or negative. We collected data from various sources, primarily Twitter. We have applied deep learning techniques and compared the results to other classifiers, and the results obtained from applying classical sentiment analysis techniques to it.
Anthology ID:
2020.winlp-1.39
Volume:
Proceedings of the Fourth Widening Natural Language Processing Workshop
Month:
July
Year:
2020
Address:
Seattle, USA
Editors:
Rossana Cunha, Samira Shaikh, Erika Varis, Ryan Georgi, Alicia Tsai, Antonios Anastasopoulos, Khyathi Raghavi Chandu
Venue:
WiNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
148–150
Language:
URL:
https://aclanthology.org/2020.winlp-1.39
DOI:
10.18653/v1/2020.winlp-1.39
Bibkey:
Cite (ACL):
Krithika Ramesh and Tanvi Anand. 2020. Outcomes of coming out: Analyzing stories of LGBTQ+. In Proceedings of the Fourth Widening Natural Language Processing Workshop, pages 148–150, Seattle, USA. Association for Computational Linguistics.
Cite (Informal):
Outcomes of coming out: Analyzing stories of LGBTQ+ (Ramesh & Anand, WiNLP 2020)
Copy Citation:
Video:
 http://slideslive.com/38929579