用深度学习分析社交媒体欺凌言论，识别高危用户
Sentiment Analysis of Cyberbullying Data in Social Media
- 采用LSTM结合BERT与OpenAI嵌入对比分析
- 在Formspring数据集上实现较高情感判别准确率
- 适合安全监控与心理干预研究者参考
社交媒体已成为现代生活的重要组成部分，但随之而来的网络欺凌问题日益严重。这种发生在社交网络上的骚扰行为随平台发展而加剧。情感分析不仅有助于识别欺凌语句，还能发现可能遭受伤害的高风险个体。本文利用深度学习与自然语言理解技术检测社交媒体中的欺凌痕迹。构建了基于长短期记忆（LSTM）单元的循环神经网络，采用不同嵌入方式：一种使用BERT嵌入，另一种替换嵌入层为OpenAI新发布的嵌入API。我们在Formspring网络欺凌数据集上比较了两种方法的性能，评估其在情感分析中的有效性。代码已公开于https://github.com/ppujari/xcs224u。
原文摘要 · Abstract (English)
Social media has become an integral part of modern life, but it has also brought with it the pervasive issue of cyberbullying a serious menace in today's digital age. Cyberbullying, a form of harassment that occurs on social networks, has escalated alongside the growth of these platforms. Sentiment analysis holds significant potential not only for detecting bullying phrases but also for identifying victims who are at high risk of harm, whether to themselves or others. Our work focuses on leveraging deep learning and natural language understanding techniques to detect traces of bullying in social media posts. We developed a Recurrent Neural Network with Long Short-Term Memory (LSTM) cells, using different embeddings. One approach utilizes BERT embeddings, while the other replaces the embeddings layer with the recently released embeddings API from OpenAI. We conducted a performance comparison between these two approaches to evaluate their effectiveness in sentiment analysis of Formspring Cyberbullying data. Our Code is Available at https://github.com/ppujari/xcs224u
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