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Interactive Multi-Grained Joint Model for Targeted Sentiment Analysis

This repository is the TensorFlow implementation of Interactive Multi-Grained Joint Model for Targeted Sentiment Analysis which was accepted by ACM CIKM 2019.

Model

Usage

Environment Setup

Just use pipenv to install requirements.

pipenv install

Train Model

pipenv run python train.py --embedding=[fasttext|glove|mock] \
                           --dataset=[rest|laptop|kkbox|twitter] \
                           --epochs=[nums]

Plot Demo Figure

pipenv run python demo.py --mock_embedding=[True|False] \
                          --dataset=[rest|laptop|kkbox|twitter]

Performance

We temporarily remove dropout layers because we don't know the correct positions they should be placed and they affect the performance dramatically.

  • Word embedding: GloVe.840B.300D
  • Model settings
    • epochs: 50
    • learning_rate: 0.001
    • dropout_rate: 0.0
    • batch_size: 32
    • kernel_size: 3
    • filter_nums: 50
    • C_tar: 3
    • C_sent: 7
    • beta: 1.0
    • gamma: 0.7

Entity Extraction

Train

Dataset Dropout Embedding Precision Recall F1
SemEval2014-Laptop 0.0 GloVe 0.768 0.531 0.628
SemEval2014-Restaurant 0.0 GloVe 0.776 0.564 0.653
Twitter 0.0 GloVe 0.998 0.996 0.997

Test

Dataset Dropout Embedding Precision Recall F1
SemEval2014-Laptop 0.0 GloVe 0.679 0.433 0.529
SemEval2014-Restaurant 0.0 GloVe 0.632 0.318 0.423
Twitter 0.0 GloVe 0.990 0.987 0.989

Sentiment Analysis

Train

Dataset Dropout Embedding Precision Recall F1
SemEval2014-Laptop 0.0 GloVe 0.743 0.507 0.603
SemEval2014-Restaurant 0.0 GloVe 0.771 0.532 0.630
Twitter 0.0 GloVe 0.957 0.954 0.955

Test

Dataset Dropout Embedding Precision Recall F1
SemEval2014-Laptop 0.0 GloVe 0.474 0.285 0.356
SemEval2014-Restaurant 0.0 GloVe 0.539 0.230 0.322
Twitter 0.0 GloVe 0.668 0.665 0.666

Case Study

We also build a sentiment clue visualization tool to find the reason why sentiment prediction was produced. You can use demo.py to produce the following figure.

Laptop_Test

Citation

You can cite this paper if you use this model

@inproceedings{Yin:2019:IMJ:3357384.3358024,
 author = {Yin, Da and Liu, Xiao and Wan, Xiaojun},
 title = {Interactive Multi-Grained Joint Model for Targeted Sentiment Analysis},
 booktitle = {Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
 series = {CIKM '19},
 year = {2019},
 isbn = {978-1-4503-6976-3},
 location = {Beijing, China},
 pages = {1031--1040},
 numpages = {10},
 url = {http://doi.acm.org/10.1145/3357384.3358024},
 doi = {10.1145/3357384.3358024},
 acmid = {3358024},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {interaction mechanism, joint model, multi-grained model, neural networks, sentiment analysis, sequence labeling},
}

License

MIT