Summary of Sentiment Analysis Of Cyberbullying Data in Social Media, by Arvapalli Sai Susmitha et al.
Sentiment Analysis of Cyberbullying Data in Social Media
by Arvapalli Sai Susmitha, Pradeep Pujari
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a deep learning-based approach to detecting cyberbullying in social media posts. It leverages recurrent neural networks with long short-term memory (LSTM) cells, utilizing different embeddings techniques to identify bullying phrases and victims at high risk of harm. The authors compare the effectiveness of two approaches: one using BERT embeddings and another replacing the embeddings layer with OpenAI’s recently released API. They evaluate their method on Formspring Cyberbullying data, highlighting its potential for sentiment analysis. The paper’s contributions lie in developing a robust approach to tackle cyberbullying, which has significant implications for social media platforms and online communities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cyberbullying is a big problem that happens on social media. It’s when someone says mean things or harasses others online. Detecting cyberbullying can help keep people safe. The researchers used special computer programs to find bullying phrases in social media posts. They compared two different ways of doing this and tested them on some data. Their goal was to make it easier for social media platforms to detect when someone is being bullied, so they can take action to stop it from happening. |
Keywords
» Artificial intelligence » Bert » Deep learning » Lstm
