Aspect-based Sentiment Classification

Introduction

This is the Tensorflow source code of our paper

Shiliang Zheng, Rui Xia. Left-Center-Right Separated Neural Network for Aspect-based Sentiment Analysis with Rotatory Attention. https://arxiv.org/abs/1802.00892.

Meanwhile, we provide our implementations of some state-of-the-art ABSC models.

If you use this package, please cite our paper.

Related Papers

1. Duyu Tang, Bing Qin, Xiaocheng Feng, and Ting Liu. Effective LSTMs for Target-Dependent Sentiment Classification with Long Short Term Memory. COLING 2016.

2. Yequan Wang, Minlie Huang, Li Zhao, and Xiaoyan Zhu. Attention-based LSTM for Aspect-level Sentiment Classification. EMNLP 2016.

3. Duyu Tang, Bing Qin, and Ting Liu. Aspect Level Sentiment Classification with Deep Memory Network. EMNLP 2016.

4. Meishan Zhang, Yue Zhang, and Duy-Tin Vo. Gated Neural Networks for Targeted Sentiment Analysis. AAAI 2016.

5. Dehong Ma, Sujian Li, Xiaodong Zhang, and Houfeng Wang. Interactive Attention Networks for Aspect-Level Sentiment Classification. IJCAI 2017.

6. Peng Chen, Zhongqian Sun, Lidong Bing, and Wei Yang. Recurrent Attention Network on Memory for Aspect Sentiment Analysis. EMNLP 2017.

7. Shiliang Zheng, Rui Xia. Left-Center-Right Separated Neural Network for Aspect-based Sentiment Analysis with Rotatory Attention. https://arxiv.org/abs/1802.00892.

source code tree

.
├── README.md
├── model
│   ├── lstm.py          Paper 1
│   ├── tc_lstm.py       Paper 1
│   ├── td_lstm.py       Paper 1
│   ├── at_lstm.py       Paper 2
│   ├── dmn_lstm.py      Paper 3
│   ├── ian.py           Paper 5
│   ├── ram.py           Paper 6
│   ├── lcr.py           Paper 7

Usage

Usage of codes:

Usage: python model/lcr.py  [options]   [parameters]
Options:
        --train_file_path
        --test_file_path
        --embedding_file_path
        --learning_rate
        --batch_size
        --n_iter
        --random_base
        --l2_reg
        --keep_prob1
        --keep_prob2

Give the usage of lcr.py for example:

python model/lcr.py --train_file_path data/absa/laptop/laptop_2014_train.txt
                    --test_file_path data/absa/laptop/laptop_2014_test.txt
                    --embedding_file_path data/absa/laptop/laptop_word_embedding_42b.txt
                    --learning_rate 0.1
                    --batch_size 25
                    --n_iter 50
                    --random_base 0.1
                    --l2_reg 0.00001
                    --keep_prob1 0.5
                    --keep_prob2 0.5