應用深度學習與自然語言處理於仇恨言論之自動偵測

Apply deep learning and natural language processing to hate speech automatic detection

蔡坤利(Kun-Li Tsai)

指導教授 ： 鄭啟斌

淡江大學/商管學院/資訊管理學系碩士班/碩士(2020年)

https://doi.org/10.6846/TKU.2020.00525

摘要

隨著網路的發展，社群媒體使用人數也逐年攀升，網路仇恨言論的問題也伴隨著發生，這個問題的影響不僅僅存在於網路，甚至影響網路使用者的身心狀況。僅管社群媒體管理方已投入大量人力與金錢試圖解決這個問題，然而仍被使用者認為成效不彰。而本研究透過深度學習與自然語言處理，使用了兩個不同的資料集，皆為內含標記仇恨言論的Twitter推文，使用了兩個深度學習模型：BERT模型與Bi-LSTM模型，透過深度學習的方式去預測Twitter推文是否為仇恨言論。本研究結果顯示，使用BERT模型進行仇恨言論偵測的成效較優於使用Bi-LSTM模型，本研究也發現，資料集內仇恨言論所佔的比例，將會影響到使用深度學習模型預測的結果。

關鍵字

BERT ； 自然語言處理 ； 仇恨言論 ； 深度學習

並列摘要

With the development of the Internet, the number of social media users has also increased year by year, and the problem of “hate speech” on the Internet has also occurred. The impact of this problem not only exists on the Internet, but also affects the physical and mental conditions of Internet users. Although social media companies have invested a lot of manpower and money in trying to solve this problem, they are still considered ineffective by users. This study uses deep learning and natural language processing to use two different data sets, both of which are Twitter tweets containing labeled hate speech. Two deep learning models are used: the BERT model and the Bi-LSTM model. Learn ways to predict whether the Twitter tweets are hate speech. The results of this study show that the performance of hate speech detection using the BERT model is better than that of the Bi-LSTM model. This study also found that the proportion of hate speech in the data set will affect the prediction results using the deep learning model.

並列關鍵字

BERT ； NLP ； hate speech ； deep learning

參考文獻

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