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.

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.

There's a significantly larger samples database available with about two million tweets.

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)

• 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 project and reimplemented the relevant parts myself.