twentiment 0.1.0

Twitter sentiment analysis tool

twentiment
==========

Research project on twitter sentiment analysis using the Naïve Bayes
Classificator.

Installation
------------

Install from PyPI (soon) or github with::

pip install -e git+https://github.com:passy/twentiment.git

Usage
-----

First, start the twentiment server that loads the data from a JSON file. A
sample is available `in the repository <https://github.com/passy/twentiment/blob/623f4064469850b40b50db4707f12a07047f022b/samples/few_tweets.json>`_.

::

twentiment_server samples/few_tweets.json

After that, you can use ``twentiment_client`` to query the server using the
syntax ``GUESS my tweet to be scored``.

Example
-------

::

twentiment> GUESS hello world
OK 0.0
twentiment> GUESS This car is amazing.
OK 0.5
twentiment> GUESS My best friend is great.
OK 0.9285714285714286
twentiment> GUESS Whatever.
OK 0.0
twentiment> GUESS This car is horrible.
OK -0.5
twentiment> GUESS I am not looking forward to my appointment tomorrow.
OK -0.9852941176470597


Wishlist
--------

(Ranked by importance)

* Have a web-frontend that searches for tweets and rates their sentiment.
* Give the server an option to fork the server process into the background
and launch a shell like twentiment_client right away.
* Restructure the Classifier to allow adaptive retraining, i.e. provide a
TRAIN command that adds new samples at runtime.
* At the moment, most of the calculations are done at start-up time, so
querying is rather cheap. Could be difficult to find a good balance.

* Persistence of the server state. Maybe through redis? Only important with
TRAIN functionality.
* Add some sort of parallelism to the server, so querying doesn't block.
* Add a way of importing live training data from twitter (like from
analysing emoticons)

Motivation
----------

This is a project report for the Business Intelligence course. To increase the
learning potential, I tried to reuse as little as possible from the excellent
`NLTK <http://nltk.org/>`_ project and reimplemented the relevant parts myself.