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        "author": "Kenneth Lim",
        "author_email": "kl545@cornell.edu",
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        "description": "AirProfile\n==========\n\n.. image:: https://badge.fury.io/py/AirProfile.svg\n    :target: https://badge.fury.io/py/AirProfile\n\nA python package for automatic analysis of Airbnb host profiles.\n\nThe package takes an Airbnb profile, automatically tags topics for each sentence, and predicts whether the profile will be perceived as more trustworthy compared to other profiles of similar length.\n\nExample Usage\n-------------\nLIWC2007_ is a proprietary dependency required for predicting trust. Unfortunately, we can't include it in this package for legal reasons.\n\n.. _LIWC2007: https://liwc.wpengine.com\n\nHowever, if you do not have LIWC, you can still perform topic classification.\n\n.. code:: python\n\n  from AirProfile import AirProfile\n\n  ap = AirProfile(liwc_path='../LIWC2007/liwc_2007.trie')\n  # or ap = AirProfile() if you do not have LIWC.\n\n  # Example Airbnb host profile.\n  input = \"\"\"I have spent my life in the service industry. I look forward to being your host and I look forward to meeting you.\"\"\"\n\n  # Segments the input at the sentence level and returns the probability that\n  # each sentence is tagged with the topics described in [1]. This works with or\n  # without LIWC.\n  ap.predict_topics(input)\n\n  >>> [\n    [\n      'i have spent my life in the service industry',\n      {\n        'relationships': 0.02,\n        'workEducation': 0.99,\n        'travel': 0.0,\n        'originResidence': 0.07,\n        'lifeMottoValues': 0.03,\n        'hospitality': 0.02,\n        'interestsTastes': 0.03,\n        'personality': 0.02\n      }\n    ], [\n      'i look forward to being your host and i look forward to meeting you',\n      {\n        'relationships': 0.0,\n        'workEducation': 0.0,\n        'travel': 0.02,\n        'originResidence': 0.0,\n        'lifeMottoValues': 0.0,\n        'hospitality': 1.0,\n        'interestsTastes': 0.0,\n        'personality': 0.04\n      }\n    ]\n  ]\n\n  # Segments the input at the sentence level and returns the probability that\n  # the profile is perceived to be more trustworthy compared to other profiles\n  # of similar length. This requires LIWC and will throw an error otherwise.\n  ap.predict_trust(input)\n\n  >>> Prediction(prob=0.49, predict=0)\n\n\nReferences\n----------\n[1] Self-disclosure and Perceived Trustworthiness of Airbnb Host Profiles. Xiao Ma, Jeff Hancock, Kenneth Lim Mingjie, and Mor Naaman. CSCW 2017. Honorable Mention for Best Paper. [PDF1_]\n\n.. _PDF1: https://s.tech.cornell.edu/assets/papers/ma2017airbnb.pdf\n\n[2] A Computational Approach to Perceived Trustworthiness of Airbnb Host Profiles. Xiao Ma, Trishala Neeraj, Mor Naamann. ICWSM 2017. Poster. [PDF2_]\n\n.. _PDF2: http://maxiao.info/assets/computational-airbnb.pdf\n\n\n",
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        "keywords": "Airbnb,self-disclosure,trustworthiness,sharing economy,social exchange",
        "license": "MIT",
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        "name": "AirProfile",
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