This is a Tensorflow implementation of Beyond Polarity: Interpretable Financial Sentiment Analysis with Hierarchical Query-driven Attention
- python 3.6.1
- Tensorflow 1.11.0
- jieba 0.39
python preprocess_train.py
python preprocess_test.pyPreprocess training dataset/test dataset. Remember to modify the dictionary, fiterwords based on your own datasets.
python train.pycd FISHQA/codeSet params based on your own datasets and train you own model
python test.py python view.py - Modify your own queries(FISHQA/Query) based on your own datasets and prior knowledge. Each
querycan be manually decided. - Notice that under folder
temp/is a subset of our preprocessed data. - As the our dataset is private, we cannot release it. We put two raw samples in folder
train_dataandtest_dataindividually. - Under folder
dictionary/, there are some extra dictionaries summarized by professional for Chinese financial news.