Research project on twitter sentiment analysis using the Naïve Bayes
Install from PyPI (soon) or github with::
pip install -e git+https://github.com:passy/twentiment.git
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>`_.
After that, you can use ``twentiment_client`` to query the server using the
syntax ``GUESS my tweet to be scored``.
twentiment> GUESS hello world
twentiment> GUESS This car is amazing.
twentiment> GUESS My best friend is great.
twentiment> GUESS Whatever.
twentiment> GUESS This car is horrible.
twentiment> GUESS I am not looking forward to my appointment tomorrow.
(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
* 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
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.
TODO: Brief introduction on what you do with files - including link to relevant help section.