Forked from: https://github.com/katakonst/sentiment-analysis-tensorflow
This is a example of sentiment analisys for romanian and english language built up on tensorflow and tflearn
-
added a webserver to expose a api
-
added Docker containers
Language parameter can be en/ro
docker run -e "language=en" --publish 8080:8001 danionescu/sentiment-analysis-tensorflow:latestcurl -X 'PUT' \
'http://localhost:8001/api/analize' \
-H 'accept: application/json' \
-H 'Content-Type: application/json' \
-d '{
"phrase": "Ceva text rau"
}'Response body:
{
"phrase": "Ceva text rau",
"language": "ro",
"positive": 0.020164920017123222,
"negative": 0.979835033416748
}cd project_folder
uvicorn webserver:app --reload --host 0.0.0.0 --port 8001
docker-compose upAfter 10 epochs for romanian dataset :
Training Step: 5360 | total loss: 0.05475 | time: 51.239s
| Adam | epoch: 010 | loss: 0.05475 - acc: 0.9881 | val_loss: 0.53884 - val_acc: 0.8536 -- iter: 17144/17144After 10 epochs for english dataset :
Training Step: 13620 | total loss: 0.05636 | time: 142.459s
| Adam | epoch: 010 | loss: 0.05636 - acc: 0.9940 | val_loss: 0.18359 - val_acc: 0.9396 -- iter: 43561/43561Pass en for English or ro for Romanian as arg to command line followed by text
$ python predict.py en "Food is awesome"
negative=0.022818154
positive=0.97718185$ python predict.py ro "Mancarea este proasta"
negative=0.9629853
positive=0.037014768Pass en for English or ro for Romanian as arg to command line
$ python train.py enPositive dataset is more numerous than the negative one. This may cause a drop in accuracy.
Dataset of reviews from yelp and imdb reviews
- neg: 10090
- pos: 33532
- total: 43561
- neg: 10228
- pos: 33394
- total: 43622
Dataset of products and movies reviews
- neg: 6370
- pos: 10774
- total: 17144
- neg: 4427
- pos: 5840
- total: 10267
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