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        "author": "Georgios Rizos",
        "author_email": "georgerizos@iti.gr",
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        "classifiers": [
            "Development Status :: 2 - Pre-Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: Apache Software License",
            "Natural Language :: English",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3 :: Only",
            "Programming Language :: Python :: Implementation",
            "Programming Language :: Python :: Implementation :: CPython",
            "Topic :: Scientific/Engineering :: Information Analysis"
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        "description": "reveal-user-classification\n==========================\n\nPerforms user classification into labels using a set of seed Twitter users with known labels and the structure of the interaction network between them.\n\nFeatures\n--------\n- Implementation of the [REVEAL FP7](http://revealproject.eu/) user-network-profile-classifier module.\n- Utilization of ARCTE algorithm for graph embedding via [reveal-graph-embedding](https://github.com/MKLab-ITI/reveal-graph-embedding).\n- Community weighting for improved graph-based user classification and via [reveal-graph-embedding](https://github.com/MKLab-ITI/reveal-graph-embedding).\n- Twitter list crowdsourcing for user annotation via [reveal-user-annotation](https://github.com/MKLab-ITI/reveal-user-annotation).\n- Messaging and communication with databases via [reveal-user-annotation](https://github.com/MKLab-ITI/reveal-user-annotation).\n\nInstall\n-------\n### Required packages\n- numpy\n- scipy\n- scikit-learn\n- networkx\n- [reveal-user-annotation](https://github.com/MKLab-ITI/reveal-user-annotation)\n- [reveal-graph-embedding](https://github.com/MKLab-ITI/reveal-graph-embedding)\n\n### Installation\nTo install for all users on Unix/Linux:\n\n    python3.4 setup.py build\n    sudo python3.4 setup.py install\n  \nAlternatively:\n\n    pip install reveal-user-classification\n\nReveal-FP7 Integration\n----------------------\nThe name of the entry point script is user_network_profile_classifier.\n\n    user_network_profile_classifier -uri $MONGO_DB_URI -id $MONGO_ASSESSMENT_ID\n    -tak $TWITTER_APP_KEY -tas $TWITTER_APP_SECRET\n    -rmquri $AMQP_URI -rmqq $AMQP_QUEUE_NAME -rmqe $AMQP_EXCHANGE -rmqrk $AMQP_ROUTING_KEY\n    -ln $LATEST_N -lts $LOWER_TIMESTAMP -uts $UPPER_TIMESTAMP\n    -nt $NUMBER_OF_PARALLEL_TASKS -nua $NUMBER_OF_USERS_TO_ANNOTATE\n    -unpcdb $USER_NETWORK_PROFILE_CLASSIFIER_MONGO_DB\n\nThe following two arguments are for establishing a connection to a Mongo database and\naccessing the documents in a collection.\n\n- $MONGO_DB_URI example: \"mongodb://admin:123456@127.0.0.1:27017\"\n\n- $MONGO_ASSESSMENT_ID example: \"new_tweets_database_name.new_tweets_collection_name\", separated by a \".\" as shown.\n\nThe following two arguments are for using a Twitter app in order to fetch data from Twitter.\n\n- $TWITTER_APP_KEY and $TWITTER_APP_SECRET: Both are taken from one's created app in the Twitter development site.\n\nThe following four arguments are for publishing messages to a RabbitMQ queue.\nThe queue is used both for publishing a \"SUCCESS\" message at completion,\nbut also for publishing the results of the module.\n\n- $AMQP_URI example: amqp://guest:guest@localhost:5672//\n- One must also supply: $AMQP_QUEUE_NAME, $AMQP_EXCHANGE and $AMQP_ROUTING_KEY\n\nThere are some optional arguments that can be considered. The following three can be used either together or apart;\notherwise all of the tweets in the collection will be read.\n\n- $LATEST_N: The N latest chronologically documents will be read from the defined collection.\n  In order for this to work properly, the \"created_at\" field of the tweets must be in the proper time format as defined by MongoDB.\n- $LOWER_TIMESTAMP: A UNIX timestamp; based on the created_at tweet field. Only tweets after this timestamp will be used for the analysis.\n- $UPPER_TIMESTAMP: Similarly, for an upper limit.\n\nThe following four arguments set various parameters for the execution of the module.\n\n- $NUMBER_OF_PARALLEL_TASKS: Number of parallel tasks initiated for each assessment analysis launch. If not specified, tries to set as number of cores.\n- $NUMBER_OF_USERS_TO_ANNOTATE: Number of users to annotate automatically, using Twitter data. Each user requires approximately at least an additional minute. Default value is 90. For faster testing, try a smaller number.\n\nSome intermediate data and the resulting user-to-topic association will be written in a Mongo database on the same Mongo client used for the input.\n\n- $USER_NETWORK_PROFILE_CLASSIFIER_MONGO_DB: A distinctive name should be chosen so as not to interfere with the databases reserved for input data. The collection in which the results are written is: \"user_topics_collection\".\n\nThe entry point script can be viewed on /reveal_user_classification/entry_points/user_network_profile_classifier.py\nwhere the argument usage can be read in greater detail.",
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        "keywords": "online-social-network user-classification Reveal-FP7",
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