Sarcasm_Detection/GRNN

• Title: Tweet Sarcasm Detection Using Deep Neural Network
• • Introduce
  • Model
  • Experimental Results
  • Installation and Environment Configuration
  • Instructions for Use
  • Reference

Title: Tweet Sarcasm Detection Using Deep Neural Network

Introduce

Sarcasm detection has been modeled as a binary document classification task, with rich features being defined manually over input documents. Traditional models employ discrete manual features to address the task, with much research effect being devoted to the design of effective feature templates. We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features. In particular, we use a bi-directional gated recurrent neural network to capture syntactic and semantic information over tweets locally, and a pooling neural network to extract contextual features automatically from history tweets.

Model

We follow previous work in the literature, building a strong discrete baseline model using features from both the target tweet itself and its contextual tweets. The structure of the model is shown in Figure 1(a), which consists of two main components, modeling the target tweet and its contextual tweets, respectively.

In contrast to the discrete model, the neural model explores low-dimensional dense vectors as input. Figure 1(b) shows the overall structure of our proposed neural model, which has two components, corresponding to the local and the contextual components of the discrete baseline model, respectively. The two components use neural network structures to extract dense real-valued features $h$ and $h^{'}$ from the local and history tweets, respectively, and we add a non-linear hidden layer to combine the neural features from the two components for classification.

Experimental Results

Installation and Environment Configuration

• Please refer to requirements.txt
• Download glove.42B.300d.txt from glove
• pip3 install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz

Instructions for Use

python3 train.py

Reference

@inproceedings{
    title = "Tweet Sarcasm Detection Using Deep Neural Network",
    author = "Zhang, Meishan  and Zhang, Yue  and Fu, Guohong",
    booktitle = "Proceedings of {COLING} 2016, the 26th International Conference on Computational Linguistics: Technical Papers",
    month = dec,
    year = "2016",
    publisher = "The COLING 2016 Organizing Committee",
    pages = "2449--2460"
    }