Exploring Lavender Tongue from Social Media Texts

Exploring Lavender Tongue from Social Media Texts

在性別與自然語言處理的脈絡下，大多數研究僅專注於生理性別的討論，對於性別文本的分類，更僅建立於異性戀男女的文本上。針對此一現象，本研究為中文性別與自然語言處理領域中，第一個由性取向的觀點出發，討論同性文本偵測的研究。首先本研究由網路論壇-PTT 收集同性戀文本並討論同性戀男女的語言學特徵。其次，藉由觀察到的語言學現象，利用5 折交叉驗證支持向量機器（Support Vector Machine）與樸素貝葉斯分類器（Naive Bayes）模型，以機器訓練的方式，利用不同的語言學特徵組來偵測男同性戀與女同性戀的網路文本。機器訓練結果顯示，在同性文本的預測上，由於本研究使用了傳統性別與自然語言處理研究未考量到的同性戀特有詞彙特徵，而在同性文本偵測上達到了較佳的正確率。

Under the issue of gender and Natural Language Processing (NLP), most papers aim at gendernorm language that spoken by biologically males and females with opposite-sex desires. However, from the point of view of sexual orientation, this study presents the first work in the task of Chinese homosexual identification. Firstly, we collect homosexual texts from social media, and secondly examine linguistic behavior found in gay and lesbian texts. In addition, we also provide sets of linguistic features to automatically predict homosexual language with the adoption of 5-fold cross-validation Support Vector Machine (SVM) and Naive Bayes (NB) models. Training procedure in the study resulted in promising f-score around 70% with the use of particular lexicon-based feature set.