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        "author": "Junpei Kawamoto",
        "author_email": "kawamoto.junpei@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Natural Language :: English",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering :: Information Analysis",
            "Topic :: Software Development :: Libraries"
        ],
        "description": "Trip Advisor Dataset Loader\n===========================\n\n|GPLv3| |Build Status| |wercker status| |Release| |PyPi| |Japanese|\n\n|Logo|\n\nFor the `Review Graph Mining project <https://github.com/rgmining>`__,\nthis package provides a loader of the `Trip Advisor\ndataset <http://times.cs.uiuc.edu/~wang296/Data/>`__ provided by `Dr.\nWang <http://www.cs.virginia.edu/~hw5x/>`__.\n\nInstallation\n------------\n\nUse pip to install this package.\n\n.. code:: shell\n\n    $ pip install --upgrade rgmining-tripadvisor-dataset\n\nNote that this installation will download a big data file from the\n`original web site <http://times.cs.uiuc.edu/~wang296/Data/>`__.\n\nThis package uses ``bz2`` internally. If your python doesn't have that\npackage (try ``import bz2``), rebuild python before installation.\n\nUsage\n-----\n\nThis package provides module ``tripadvisor`` and this module provides\n``load`` function. The ``load`` function takes a graph object which\nimplements the `graph\ninterface <https://rgmining.github.io/dataset-io/modules/dataset_io.html#graph-interface>`__\ndefined in `Review Graph Mining\nproject <https://github.com/rgmining>`__.\n\nFor example, the following code constructs a graph object provides the\n`FRAUDAR <http://www.kdd.org/kdd2016/subtopic/view/fraudar-bounding-graph-fraud-in-the-face-of-camouflage>`__\nalgorithm, loads the Trip Advisor dataset, runs the algorithm, and then\noutputs names of anomalous reviewers. Since this dataset consists of\nhuge reviews, loading may take long time.\n\n.. code:: py\n\n    import fraudar\n    import tripadvisor\n\n    # Construct a graph and load the dataset.\n    graph = fraudar.ReviewGraph()\n    tripadvisor.load(graph)\n\n    # Run the analyzing algorithm.\n    graph.update()\n\n    # Print names of reviewers who are judged as anomalous.\n    for r in graph.reviewers:\n      if r.anomalous_score == 1:\n        print r.name\n\n    # The number of reviewers the dataset has: -> 1169456.\n    len(graph.reviewers)\n\n    # The number of reviewers judged as anomalous: -> 147.\n    len([r for r in graph.reviewers if r.anomalous_score == 1])\n\nNote that you may need to install the FRAUDAR algorithm for the Review\nMining Project by ``pip install rgmining-fraudar``.\n\nLicense\n-------\n\nThis software is released under The GNU General Public License Version\n3, see `COPYING <COPYING>`__ for more detail.\n\nThe authors of the Trip Advisor dataset, which this software imports,\nrequires to cite the following papers when you publish research papers\nusing this package:\n\n-  `Hongning Wang <http://www.cs.virginia.edu/~hw5x/>`__, `Yue\n   Lu <https://www.linkedin.com/in/yue-lu-80a6a549>`__, and `ChengXiang\n   Zhai <http://czhai.cs.illinois.edu/>`__, \"`Latent Aspect Rating\n   Analysis without Aspect Keyword\n   Supervision <http://times.cs.uiuc.edu/~wang296/paper/p618.pdf>`__,\"\n   In Proc. of the 17th ACM SIGKDD Conference on Knowledge Discovery and\n   Data Mining (KDD'2011), pp.618-626, 2011;\n-  `Hongning Wang <http://www.cs.virginia.edu/~hw5x/>`__, `Yue\n   Lu <https://www.linkedin.com/in/yue-lu-80a6a549>`__, and `Chengxiang\n   Zhai <http://czhai.cs.illinois.edu/>`__, \"`Latent Aspect Rating\n   Analysis on Review Text Data: A Rating Regression\n   Approach <http://sifaka.cs.uiuc.edu/~wang296/paper/rp166f-wang.pdf>`__,\"\n   In Proc. of the 16th ACM SIGKDD Conference on Knowledge Discovery and\n   Data Mining (KDD'2010), pp.783-792, 2010.\n\n.. |GPLv3| image:: https://img.shields.io/badge/license-GPLv3-blue.svg\n   :target: https://www.gnu.org/copyleft/gpl.html\n.. |Build Status| image:: https://travis-ci.org/rgmining/tripadvisor.svg?branch=master\n   :target: https://travis-ci.org/rgmining/tripadvisor\n.. |wercker status| image:: https://app.wercker.com/status/815b060cc2fa911215674efdc1078d50/s/master\n   :target: https://app.wercker.com/project/byKey/815b060cc2fa911215674efdc1078d50\n.. |Release| image:: https://img.shields.io/badge/release-0.5.6-brightgreen.svg\n   :target: https://github.com/rgmining/tripadvisor/releases/tag/v0.5.6\n.. |PyPi| image:: https://img.shields.io/badge/pypi-0.5.6-brightgreen.svg\n   :target: https://pypi.python.org/pypi/rgmining-tripadvisor-dataset\n.. |Japanese| image:: https://img.shields.io/badge/qiita-%E6%97%A5%E6%9C%AC%E8%AA%9E-brightgreen.svg\n   :target: http://qiita.com/jkawamoto/items/86c687d85efb783bcd7d\n.. |Logo| image:: https://rgmining.github.io/tripadvisor/_static/image.png\n   :target: https://rgmining.github.io/tripadvisor/",
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