# Detecting state of aggression in sentences using CNN

**Authors:** Rodmonga Potapova, Denis Gordeev  
**arXiv:** [1604.06650](https://arxiv.org/abs/1604.06650)  
**Date:** 2016-04-22  
**Summary page:** https://www.summarizepaper.com/en/paper/1604.06650v1/

## Key points

- The study explores verbal expression of aggression and its detection through machine learning and neural network methods.
- A corpus of messages from anonymous imageboards was used to test the results.
- The effectiveness of a Random Forest classifier was compared with a convolutional neural network for the "Movie reviews with one sentence per review" corpus.
- This research has implications for identifying aggressive language in online communication, which can be used to prevent cyberbullying and hate speech.
- Machine learning and neural networks can be utilized to detect aggression in written communication.
- The study highlights the potential of these technologies for detecting aggression in written communication and preventing cyberbullying and hate speech.

## Summary

In the article "Detecting state of aggression in sentences using CNN," authors Rodmonga Potapova and Denis Gordeev explore the verbal expression of aggression and its detection through machine learning and neural network methods. The study utilizes a corpus of messages from anonymous imageboards to test their results. Additionally, they compare the effectiveness of a Random Forest classifier with a convolutional neural network for the "Movie reviews with one sentence per review" corpus. This research has implications for identifying aggressive language in online communication, which can be used to prevent cyberbullying and hate speech. It also provides insight into how machine learning and neural networks can be utilized to detect aggression in written communication. Overall, this study highlights the potential of these technologies for detecting aggression in written communication and preventing cyberbullying and hate speech.