Sentiment analysis is a REST API for opinion mining (also known as sentiment analysis) of spanish comments. It analizes a comment, tweet, post or sentence and determines sentiment polarity such as negative, positive or neutral according to the vocabulary used. With this API you can:
- Analize a single comment and obtain sentiment polarity (positive, negative, neutral)
- Analize multiple comments and obtain sentiment polarity of each of these comments
- For a set of comments retrieve overall-sentiment polarity
- For a set of comments retrieve statistics about sentiment orientation
- For a set of comments retrieve a bag of words (folksonomy) related to sentiment orientation
0.1.0
download source file from bit bucket [spribo_content/sentiment-analysis]###Dependencies:
python (2.7)
pattern (2.6)
django (1.6.3)
djangorestframework (2.3.13)
django-rest-swagger (0.1.14)###Unix-based: It is highly recommended to use virtualenv:
sudo pip install virtualenvCreate the directory for deployment and activate it:
sudo virtualenv /your_path/deployment_venv
cd /your_path/deployment_venv
source bin/activateCopy sentiment directory into deployment_env directory, project should look like:
deployment_env
|-- sentiment
| |-- manage.py
| |-- tmp.db
| |-- sentiment
| |-- comments
| | `-- classifier
| |-- static-files
| |-- rest-framework-swagger
|-- bin
|-- lib
|-- includeInstall dependencies:
pip install pattern
pip install django
pip install djangorestframework
pip install django-rest-swagger####Using GUNICORN for dynamic content:
pip install gunicorn
cd sentiment
gunicorn sentiment.wsgi:applicationAnd now our service is running in http://localhost:8000 or http://127.0.0.1:8000
To chance port use:
gunicorn sentiment.wsgi:application --bind=127.0.0.1:8001####Using NGINX for static content (pretty browsable interface)
For Linux
sudo apt-get install nginxFor Mac OS, firt install git then homebrew:
sudo mkdir /usr/local
sudo chown -R `_username_` /usr/local
curl -L http://github.com/mxcl/homebrew/tarball/master | tar xz --strip 1 -C /usr/local
brew install git
cd /usr/local
git init
git remote add origin git://github.com/mxcl/homebrew.git
git pull origin masterThen install NGINX:
brew install nginxCopy the STATIC_ROOT of settings.py
STATIC_ROOT = os.path.join(BASE_DIR, 'static-files')Here BASE_DIR is the direction of deployment directory.(This addres is printed in the bash as /context_address/deployment_env/static-files) Then, modify config nginx file (make backup of config file, just in case)
cd /usr/local/etc/nginx/
vim nginx.configModify followig parameters:
server{
listen 8000;
server_name 'your-fully-qualified-domain-name.com';
}
location /{
proxy_pass http://127.0.0.1:8001;
}
location /static/{
autoindex on;
alias 'here you put static_root directory'
}
To see more options of NGINX configuration see nginx conf
Then link config file
mkdir sites-enabled
sudo ln nginx.config sites-enabled/any_project_nameStart servers, first nginx:
sudo start nginxor:
launchctl load ~/Library/LaunchAgents/homebrew.mxcl.nginx.plistAnd run gunicorn as daemon:
gunicorn sentiment.wsgi:application --bind=127.0.0.1:8001 --daemon###Deploying in Windows with Apache and mod_wsgi: Deploying with Apache
Actually this API support two methods: GET and POST.
###GET: URL:
http://yourdomain.com/commentsQUERY_PARAMS
comment: commet string (mandatory)
classifier_type: SVM or MNB (optional)
no_classes: 2 or 5 (optional)EXAMPLE_URL :
http://localhost:8000/comments?comment=%27este+es+un+comentario+muy+malo%27&classifier_type=MNB&no_classes=2You can also use from bash:
curl http://localhost:8000/comments?comment=%27este+es+un+buen+comentario%27###POST:
Either in the browser or in bash:
curl -X POST http://localhost:8000/comments -d 'JSON_FORMAT' -H "Content-Type: application/json"
JSON FORMAT:
{
"control": {
"classifier": "automatic",
"no_classes": "default",
"response": {"folksonomies":true, "comments":"full"}
},
"data": [
{
"comment": "primer comentario",
"id": 1
},
{
"comment": "segundo comentario",
"id": 2
},
{
"comment": "n-esimo comentario",
"id": 99
}
]
}We define 2 parts: control and data
-
classifier "automatic" for automatic selection of classifiers "SVM" classifier 88% precision less than 50 words "MNB" classifier 77% precision any amount of words
-
no_classes "default" means 5 classes (muy_negativo, negativo, neutro, positivo, muy_positivo) "quick" means 2 classes (negativo, positivo)
-
response "folksonomies" if true returns folksonomies (remember json true/false are lowercase) "comments" if "ids_only" returns ids_only polarity, if "full" returns comment by comment polarity if "nothing" returns nothing
####Data:
- All comments with their respectives id's
Request example:
curl -X POST http://127.0.0.1:8000/comments -d '{"control": {"classifier": "automatic", "no_classes": "default", "response": {"folksonomies":true, "comments":"full"}}, "data": [{"comment": "primer comentario", "id": 1}, {"comment": "segundo comentario", "id": 2}, {"comment": "n-esimo comentario", "id": 99}]}' -H "Content-Type: application/json"Response example:
{
"Overall Sentiment": 2.0,
"Statistics": {
"positivos": 0,
"neutros": 0,
"muy_positivos": 3,
"muy_negativos": 0,
"negativos": 0
},
"Comments": [
{
"comment": "primer comentario",
"polarity": "muy_positivo",
"id": 1,
"ranking": 5
},
{
"comment": "segundo comentario",
"polarity": "muy_positivo",
"id": 2,
"ranking": 5
},
{
"comment": "n-esimo comentario",
"polarity": "muy_positivo",
"id": 99,
"ranking": 5
}
],
"folksonomies": [
{
"muy_negativos": {}
},
{
"negativos": {}
},
{
"neutros": {}
},
{
"positivos": {}
},
{
"muy_positivos": {
"n-esimo comentario": 1,
"comentario": 3,
"n-esimo": 1
}
}
]
}