Deep Learning Models for Chinese Sentiment Analysis

Introduction

Effective representation of a text is critical for various natural language processing tasks. For the particular task of Chinese sentiment analysis, it is important to understand and choose an effective representation of a text from different forms of Chinese representations such as word, character and pinyin. As illustrated in the paper of "Exploiting Effective Representations for Chinese Sentiment Analysis Using a Multi-Channel Convolutional Neural Network", the character representation is efficient in terms of small vocabulary size and requires the least pre-processing effort without the need for Chinese word segmentation. This project implements the baseline models, including CNN, LSTM, self-attention and BERT for Chinese sentiment analysis based on the character representation.

Dataset

This repository releases the dataset crawled from Amazon reviews, where each review has a star (1-5) rated by a customer. To evaluate Chinese sentiment analysis, each review was labeled as positive (star 4, 5), negative (star 1, 2) and neutral (star 3), and was tokenized into characters for feeding the models.

	#positive	#neutral	#negative
train	46982	4272	4854
test	11710	1084	1233

Installation

Please setup a conda environment and install the required packages.

pip install -r requirements.txt

Experiments

To evaluate the models, the experiments can be run in batch as follows:

bash batch.sh

Experimental results

	Accuracy
CNN	87.03%
CNN+SA	87.20%
LSTM	87.37%
LSTM+SA	87.17%
BERT	88.69%

References:

For details of the implemented models, please refer to the following papers:

@article{liu2018exploiting,
  title={Exploiting effective representations for chinese sentiment analysis using a multi-channel convolutional neural network},
  author={Liu, Pengfei and Zhang, Ji and Leung, Cane Wing-Ki and He, Chao and Griffiths, Thomas L},
  journal={arXiv preprint arXiv:1808.02961},
  year={2018}
}

@inproceedings{kim-2014-convolutional,
    title = "Convolutional Neural Networks for Sentence Classification",
    author = "Kim, Yoon",
    booktitle = "Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing ({EMNLP})",
    month = oct,
    year = "2014",
    address = "Doha, Qatar",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D14-1181",
    doi = "10.3115/v1/D14-1181",
    pages = "1746--1751",
}

@article{vaswani2017attention,
  title={Attention is all you need},
  author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N and Kaiser, {\L}ukasz and Polosukhin, Illia},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}

@article{devlin2018bert,
  title={Bert: Pre-training of deep bidirectional transformers for language understanding},
  author={Devlin, Jacob and Chang, Ming-Wei and Lee, Kenton and Toutanova, Kristina},
  journal={arXiv preprint arXiv:1810.04805},
  year={2018}
}

Report

Please feel free to create an issue or send emails to the author at ppfliu@gmail.com.

ppfliu/sentiment-analysis