Metadata-Version: 2.0
Name: ParallelDots
Version: 1.0.16
Summary: Python Wrapper for ParallelDots APIs
Home-page: https://github.com/ParallelDots/ParallelDots-Python-API.git
Author: Ahwan Kumar,Meghdeep Ray
Author-email: ahwan@paralleldots.com,meghdeepr@paralleldots.com
License: MIT
Keywords: paralleldots sentiment taxonomy ner semantic similarity deeplearning intent emotion abuse
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.0
Classifier: Programming Language :: Python :: 3.1
Classifier: Programming Language :: Python :: 3.2
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Dist: requests

ParallelDots-Python-API
=======================

A wrapper for the `ParallelDots API <http://www.paralleldots.com>`__.

Installation
------------

>From PyPI:

::

    pip install paralleldots

>From Source:

::

    https://github.com/ParallelDots/ParallelDots-Python-API.git
    python setup.py install

API Keys & Setup
----------------

Signup and get your free API key from
`ParallelDots <http://www.paralleldots.com/pricing>`__. You will receive
a mail containing the API key at the registered email id.

Configuration:

::

    >>> from paralleldots import set_api_key, get_api_key

    # Setting your API key
    >>> set_api_key("YOUR API KEY")

    # Viewing your API key
    >>> get_api_key()

Supported APIs:
---------------

-  `Semantic Similarity <https://tinyurl.com/k23nqs9>`__
-  `Sentiment Analysis <https://tinyurl.com/km99mzb>`__
-  Taxonomy
-  `Named Entity Extraction/Recognition ( NER
   ) <https://tinyurl.com/k9yglwc>`__
-  `Keywords <https://tinyurl.com/kujcu8o>`__
-  `Intent <https://tinyurl.com/n568bqw>`__
-  `Emotion <http://blog.paralleldots.com/technology/deep-learning/emotion-detection-using-machine-learning/>`__
-  Abuse
-  Multiple Language Sentiment ( The function encodes the input text )

   -  Portuguese ( pt )
   -  Chinese ( cn )
   -  Spanish ( sp )
- Sentiment social
- Usage

Examples
--------

::

    >>> from paralleldots import similarity, ner, taxonomy, sentiment, keywords, intent, emotion, multilang, abuse, sentiment_social

    >>> similarity( "Sachin is the greatest batsman", "Tendulkar is the finest cricketer" )
    {"actual_score": 0.842932,"normalized_score": 4.931469}

    >>> sentiment( "Come on, lets play together" )
    {"probabilities": {"positive": 0.00002,"neutral": 0.999954,"negative": 0.000026}}

    >>> taxonomy( "Narendra Modi is the prime minister of India" )
    {"tag": "terrorism", "confidence_score": 0.531435}, {"tag": "world politics", "confidence_score": 0.391963}, {"tag": "politics", "confidence_score": 0.358955}, {"tag": "religion", "confidence_score": 0.308195}, {"tag": "defense", "confidence_score": 0.26187}, {"tag": "business", "confidence_score": 0.20885}, {"tag": "entrepreneurship", "confidence_score": 0.18349}, {"tag": "health", "confidence_score": 0.171121}, {"tag": "technology", "confidence_score": 0.168591}, {"tag": "law", "confidence_score": 0.156953}, {"tag": "education", "confidence_score": 0.146511}, {"tag": "science", "confidence_score": 0.101002}, {"tag": "crime", "confidence_score": 0.085016}, {"tag": "entertainment", "confidence_score": 0.080634}, {"tag": "environment", "confidence_score": 0.078024}, {"tag": "disaster", "confidence_score": 0.075295}, {"tag": "weather", "confidence_score": 0.06784}, {"tag": "accident", "confidence_score": 0.066831}, {"tag": "sports", "confidence_score": 0.058329}, {"tag": "advertising", "confidence_score": 0.054868}, {"tag": "history", "confidence_score": 0.043581}, {"tag": "mining", "confidence_score": 0.03833}, {"tag": "travel", "confidence_score": 0.025517}, {"tag": "geography", "confidence_score": 0.022372}, {"tag": "nature", "confidence_score": 0.013477}, {"tag": "lifestyle", "confidence_score": 0.006467}, {"tag": "automobile", "confidence_score": 0.001161}, {"tag": "personal care", "confidence_score": 0.000275}]}

    >>> ner( "Narendra Modi is the prime minister of India" )
    {"entities": [
        {
            "category": "name",
            "name": "Narendra Modi",
            "confidence_score": 0.951439
        },
        {
            "category": "place",
            "name": "India",
            "confidence_score": 0.9263
        }
    ]}

    >>> keywords( "Prime Minister Narendra Modi tweeted a link to the speech Human Resource Development Minister Smriti Irani made in the Lok Sabha during the debate on the ongoing JNU row and the suicide of Dalit scholar Rohith Vemula at the Hyderabad Central University." )
    [{"relevance_score": 4, "keyword": "Prime Minister Narendra Modi"}, {"relevance_score": 1, "keyword": "link"}, {"relevance_score": 3, "keyword": "speech Human Resource"}, {"relevance_score": 1, "keyword": "Smriti"}, {"relevance_score": 1, "keyword": "Lok"}]

    >>> emotion("Did you hear the latest Porcupine Tree song ? It's rocking !")
    {"emotion": "other", "probabilities": {"angry": 0.010629, "other": 0.453988, "sad": 0.028748, "excited": 0.2596, "happy": 0.247035}
    >>> intent("Finance ministry calls banks to discuss new facility to drain cash")
    {"probabilities": {"news": 0.946028, "other": 0.015853, "query": 0.000412, "feedback/opinion": 0.014115, "spam": 0.023591}}

    >>> multilang("Me encanta jugar al baloncesto", "es")   # The text is encoded in the function
    {"sentiment": "positive", "confidence_score": 1.0}

    >>> abuse("you f**king a$$hole")
    {"sentence_type": "Abusive", "confidence_score": 0.953125}

    >>> sentiment_social("I left my camera at home")
    {"probabilities": {"positive": 0.040374, "neutral": 0.491032, "negative": 0.468594}}

    >>> usage()
    {
    "emotion": 100,
    "sentiment": 100,
    "similarity": 100,
    "taxonomy": 100,
    "abuse": 100,
    "intent": 100,
    "keywords": 100,
    "ner": 100,
    "multilang": 100,
    "sentiment_social": 100
    }


