Attention Mechanism for Aggressive Detection

This paper describes the system we developed for IberEval 2018 on Aggressive detection track of Authorship and Aggressiveness Analysis on Twitter (MEX-A3T). The task focuses on the detection of aggressive comments in tweets that come from Mexican users. Systems must be able to determine whether a tweet is aggressive or not. Our approach is an Attention-based Long Short-Term Memory Network Recurrent Neural Network where the attention layer helps to calculate the contribution of each word towards targeted aggressive classes. In particular, we build a Bidireccional LSTM to extract information from the word embeddings over the sentence, then apply attention over the hidden states to estimate the importance of each word and finally feed this context vector to another LSTM model to estimate whether the tweet is aggressive or not. The experimental results show that our model achieves outstanding results.