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

翻译：社交媒体已成为现代生活不可或缺的一部分，但也随之带来了网络欺凌这一普遍问题——当今数字时代的一个严重威胁。网络欺凌作为一种发生在社交网络上的骚扰形式，已随着这些平台的发展而不断升级。情感分析不仅对检测欺凌性短语具有重要潜力，还能识别那些对自身或他人具有高伤害风险的受害者。我们的工作重点是利用深度学习和自然语言理解技术来检测社交媒体帖子中的欺凌痕迹。我们开发了一种采用长短期记忆（LSTM）单元的循环神经网络，并使用了不同的嵌入方法。其中一种方法采用BERT嵌入，而另一种方法则将嵌入层替换为OpenAI最新发布的嵌入API。我们对这两种方法进行了性能比较，以评估它们在Formspring网络欺凌数据情感分析中的有效性。我们的代码发布于https://github.com/ppujari/xcs224u。