NIHRIO at SemEval-2018 Task 3: A Simple and Accurate Neural Network Model for Irony Detection in Twitter
This work addresses the problem of automated irony detection in social media for natural language processing applications, but it is incremental as it builds on existing neural network approaches with feature engineering.
The paper tackled irony detection in English tweets by proposing a simple neural network model with multiple input features, achieving third in accuracy and fifth in F1 score in the SemEval-2018 Task 3 competition.
This paper describes our NIHRIO system for SemEval-2018 Task 3 "Irony detection in English tweets". We propose to use a simple neural network architecture of Multilayer Perceptron with various types of input features including: lexical, syntactic, semantic and polarity features. Our system achieves very high performance in both subtasks of binary and multi-class irony detection in tweets. In particular, we rank third using the accuracy metric and fifth using the F1 metric. Our code is available at https://github.com/NIHRIO/IronyDetectionInTwitter