https://github.com/jeonghunyoon/text-classification-tensorflow
Tensorflow highlevel api implementation of "Convolutional Neural Networks for Sentence Classification"
https://github.com/jeonghunyoon/text-classification-tensorflow
cnn neural-network semantic-analysis tensorflow tensorflow-experiments tensorflow-high-level-apis text-classification
Last synced: 4 months ago
JSON representation
Tensorflow highlevel api implementation of "Convolutional Neural Networks for Sentence Classification"
- Host: GitHub
- URL: https://github.com/jeonghunyoon/text-classification-tensorflow
- Owner: jeonghunyoon
- Created: 2018-09-10T16:15:04.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-12-07T14:00:36.000Z (over 7 years ago)
- Last Synced: 2025-02-13T18:49:32.301Z (over 1 year ago)
- Topics: cnn, neural-network, semantic-analysis, tensorflow, tensorflow-experiments, tensorflow-high-level-apis, text-classification
- Language: Python
- Homepage:
- Size: 10.5 MB
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Text-classification-tensorflow
This project is **Sentiment Analysis** for Naver(Korean portal site) movie review.
It uses **Tensorflow highlevel api** and `CNN`, `RNN` models for classification.
For simple keras implementation, please refer to
https://github.com/jeonghunyoon/spam-classification-cnn-tf.
## Data
https://github.com/e9t/nsmc
## References
This project is based on following papers.
- http://www.aclweb.org/anthology/D14-1181
- https://arxiv.org/abs/1510.03820
## Run project
```python
python executor.py is_first_time parse_type embedding_type
# command example : python executor.py false morphs word2vec
```
- is_first_time : *true* or *false*. When *true* it will create all parsed files(morphs, nouns)
and store it. When *false* it just load the parsed data according to parse_type.
- parse_type : *morphs* or *nouns*
- embedding_type : *doc2vec* or *word2vec*. LDA, NMF will be added.
## Performance
- parse_type : morphs, embedding_type : word2vec
- accuracy = 0.8116487, area_under_auc = 0.8694117
## Author
Jeonghun Yoon