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    "info": {
        "author": "Pascal Hartig",
        "author_email": "phartig@rdrei.net",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Developers",
            "License :: OSI Approved :: Apache Software License",
            "Natural Language :: English",
            "Operating System :: Unix",
            "Programming Language :: Python :: 3.2",
            "Programming Language :: Python :: 3.3",
            "Topic :: Communications",
            "Topic :: Internet :: WWW/HTTP"
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        "description": "twentiment\n==========\n\nResearch project on twitter sentiment analysis using the Na\u00efve Bayes\nClassificator.\n\nInstallation\n------------\n\nInstall from PyPI (soon) or github with::\n\n    pip install -e git+https://github.com:passy/twentiment.git\n\nUsage\n-----\n\nFirst, start the twentiment server that loads the data from a JSON file. A\nsample is available `in the repository <https://github.com/passy/twentiment/blob/623f4064469850b40b50db4707f12a07047f022b/samples/few_tweets.json>`_.\n\n::\n\n    twentiment_server samples/few_tweets.json\n\nAfter that, you can use ``twentiment_client`` to query the server using the\nsyntax ``GUESS my tweet to be scored``.\n\nExample\n-------\n\n::\n\n    twentiment> GUESS hello world\n    OK 0.0\n    twentiment> GUESS This car is amazing.\n    OK 0.5\n    twentiment> GUESS My best friend is great.\n    OK 0.9285714285714286\n    twentiment> GUESS Whatever.\n    OK 0.0\n    twentiment> GUESS This car is horrible.\n    OK -0.5\n    twentiment> GUESS I am not looking forward to my appointment tomorrow.\n    OK -0.9852941176470597\n\n\nWishlist\n--------\n\n(Ranked by importance)\n\n    * Have a web-frontend that searches for tweets and rates their sentiment.\n    * Give the server an option to fork the server process into the background\n      and launch a shell like twentiment_client right away.\n    * Restructure the Classifier to allow adaptive retraining, i.e. provide a\n      TRAIN command that adds new samples at runtime.\n        * At the moment, most of the calculations are done at start-up time, so\n          querying is rather cheap. Could be difficult to find a good balance.\n\n    * Persistence of the server state. Maybe through redis? Only important with\n      TRAIN functionality.\n    * Add some sort of parallelism to the server, so querying doesn't block.\n    * Add a way of importing live training data from twitter (like from\n      analysing emoticons)\n\nMotivation\n----------\n\nThis is a project report for the Business Intelligence course. To increase the\nlearning potential, I tried to reuse as little as possible from the excellent\n`NLTK <http://nltk.org/>`_ project and reimplemented the relevant parts myself.",
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        "license": "Licensed under the Apache License, Version 2.0 (the 'License');\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n   http://www.apache.org/licenses/LICENSE-2.0\n\n   Unless required by applicable law or agreed to in writing, software\n   distributed under the License is distributed on an 'AS IS' BASIS,\n   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n   See the License for the specific language governing permissions and\n   limitations under the License.",
        "maintainer": null,
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        "name": "twentiment",
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