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        <title>Outcomes of coming out: Analyzing stories of LGBTQ+</title>
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        <namePart type="given">Krithika</namePart>
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            <title>Proceedings of the Fourth Widening Natural Language Processing Workshop</title>
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    <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.</abstract>
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        <date>2020-07</date>
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            <start>148</start>
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%0 Conference Proceedings
%T Outcomes of coming out: Analyzing stories of LGBTQ+
%A Ramesh, Krithika
%A Anand, Tanvi
%Y Cunha, Rossana
%Y Shaikh, Samira
%Y Varis, Erika
%Y Georgi, Ryan
%Y Tsai, Alicia
%Y Anastasopoulos, Antonios
%Y Chandu, Khyathi Raghavi
%S Proceedings of the Fourth Widening Natural Language Processing Workshop
%D 2020
%8 July
%I Association for Computational Linguistics
%C Seattle, USA
%F ramesh-anand-2020-outcomes
%X 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.
%R 10.18653/v1/2020.winlp-1.39
%U https://aclanthology.org/2020.winlp-1.39
%U https://doi.org/10.18653/v1/2020.winlp-1.39
%P 148-150

Markdown (Informal)

[Outcomes of coming out: Analyzing stories of LGBTQ+](https://aclanthology.org/2020.winlp-1.39) (Ramesh & Anand, WiNLP 2020)

• Outcomes of coming out: Analyzing stories of LGBTQ+ (Ramesh & Anand, WiNLP 2020)

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.