This tool works by examining individual words and short sequences of words (n-grams) and comparing them with a probability model. The probability model is built on a prelabeled test set of IMDb movie reviews. It can also detect negations in phrases, i.e, the phrase "not bad" will be classified as positive despite having two individual words with a negative sentiment. The web service uses a coroutine server based on gevent, so that the trained database can be loaded into shared memory for all requests, which makes it quite scalable and fast. The API is specified here, it supports batch calls so that network latency isn't the main bottleneck.

You can read more about the details of the model in this paper . The code for the training module is also open source and available on Github .

AUTHOR: Vivek Narayanan < vivek_n@me.com >

LICENSE: BSD

Setting up the server is a fairly straightforward task, here are the steps:

1. Install pip, the python package manager.
2. cd to the directory containing the sentiment code and run pip install -r requirements.txt . This will install the dependencies.
3. Install redis and start the program redis-server. Eg: redis-server --daemonize yes. Redis is used here only for tracking/stats purposes, if you don't want it remove all references to redis in the code.
4. Finally, create a file called "config.py" and set the parameters as in the example below.

    HOST="http://ec2-54-xxxx.us-west-2.compute.amazonaws.com" 
    PORT=80 
    STATS_KEY="sentiment_stats" 
    RHOST=''
    RPORT=6379 
    RPASS=None

HOST and PORT refer to where you want to host the python server STATS_KEY is the prefix used for the redis entries, RHOST, RPORT are RPASS are the host, port and password of the redis server.

Run the server by executing the command nohup python run.py &