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https://github.com/gsi-upm/senpy
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107 lines
3.8 KiB
ReStructuredText
107 lines
3.8 KiB
ReStructuredText
Welcome to Senpy's documentation!
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=================================
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.. image:: https://readthedocs.org/projects/senpy/badge/?version=latest
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:target: http://senpy.readthedocs.io/en/latest/
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.. image:: https://badge.fury.io/py/senpy.svg
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:target: https://badge.fury.io/py/senpy
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.. image:: https://travis-ci.org/gsi-upm/senpy.svg
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:target: https://github.com/gsi-upm/senpy/senpy/tree/master
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.. image:: https://img.shields.io/pypi/l/requests.svg
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:target: https://lab.gsi.upm.es/senpy/senpy/
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Senpy is a framework to build sentiment and emotion analysis services.
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It provides functionalities for:
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- developing sentiment and emotion classifier and exposing them as an HTTP service
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- requesting sentiment and emotion analysis from different providers (i.e. Vader, Sentimet140, ...) using the same interface (:doc:`apischema`). In this way, applications do not depend on the API offered for these services.
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- combining services that use different sentiment model (e.g. polarity between [-1, 1] or [0,1] or emotion models (e.g. Ekkman or VAD)
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- evaluating sentiment algorithms with well known datasets
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Using senpy services is as simple as sending an HTTP request with your favourite tool or library.
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Let's analyze the sentiment of the text "Senpy is awesome".
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We can call the `Sentiment140 <http://www.sentiment140.com/>`_ service with an HTTP request using curl:
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.. code:: shell
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:emphasize-lines: 14,18
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$ curl "http://senpy.gsi.upm.es/api/sentiment140" \
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--data-urlencode "input=Senpy is awesome"
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{
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"@context": "http://senpy.gsi.upm.es/api/contexts/YXBpL3NlbnRpbWVudDE0MD8j",
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"@type": "Results",
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"entries": [
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{
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"@id": "prefix:",
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"@type": "Entry",
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"marl:hasOpinion": [
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{
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"@type": "Sentiment",
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"marl:hasPolarity": "marl:Positive",
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"prov:wasGeneratedBy": "prefix:Analysis_1554389334.6431913"
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}
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],
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"nif:isString": "Senpy is awesome",
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"onyx:hasEmotionSet": []
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}
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]
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}
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Congratulations, you’ve used your first senpy service!
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You can observe the result: the polarity is positive (marl:Positive). The reason of this prefix is that Senpy follows a linked data approach.
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You can analyze the same sentence using a different sentiment service (e.g. Vader) and requesting a different format (e.g. turtle):
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.. code:: shell
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$ curl "http://senpy.gsi.upm.es/api/sentiment-vader" \
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--data-urlencode "input=Senpy is awesome" \
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--data-urlencode "outformat=turtle"
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@prefix : <http://www.gsi.upm.es/onto/senpy/ns#> .
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@prefix endpoint: <http://senpy.gsi.upm.es/api/> .
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@prefix marl: <http://www.gsi.dit.upm.es/ontologies/marl/ns#> .
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@prefix nif: <http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif-core#> .
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@prefix prefix: <http://senpy.invalid/> .
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@prefix prov: <http://www.w3.org/ns/prov#> .
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@prefix senpy: <http://www.gsi.upm.es/onto/senpy/ns#> .
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prefix: a senpy:Entry ;
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nif:isString "Senpy is awesome" ;
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marl:hasOpinion [ a senpy:Sentiment ;
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marl:hasPolarity "marl:Positive" ;
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marl:polarityValue 6.72e-01 ;
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prov:wasGeneratedBy prefix:Analysis_1562668175.9808676 ] .
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[] a senpy:Results ;
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prov:used prefix: .
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As you see, Vader returns also the polarity value (0.67) in addition to the category (positive).
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If you are interested in consuming Senpy services, read :doc:`Quickstart`.
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To get familiar with the concepts behind Senpy, and what it can offer for service developers, check out :doc:`development`.
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:doc:`apischema` contains information about the semantic models and vocabularies used by Senpy.
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.. toctree::
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:caption: Learn more about senpy:
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:maxdepth: 2
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:hidden:
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senpy
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demo
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Quickstart.ipynb
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installation
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conversion
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Evaluation.ipynb
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apischema
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development
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publications
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projects
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