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API and Schema
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##############
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.. toctree::
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vocabularies.rst
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api.rst
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schema.rst
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Welcome to Senpy's documentation!
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=================================
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Contents:
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With Senpy, you can easily turn your sentiment or emotion analysis algorithm into a full blown semantic service.
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Sharing your sentiment analysis with the world has never been easier.
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Senpy provides:
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* Parameter validation, error handling
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* Formatting: JSON-LD, Turtle/n-triples input and output, or simple text input
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* Linked Data. Results are semantically annotated, using a series of well established vocabularies, and sane default URIs.
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* A web UI where users can explore your service and test different settings
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* A client to interact with any senpy service
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* A command line tool
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.. toctree::
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:caption: Learn more about senpy
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:maxdepth: 2
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senpy
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installation
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usage
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api
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schema
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apischema
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plugins
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conversion
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demo
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:maxdepth: 2
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research.rst
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Research
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--------
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If you use Senpy in your research, please cite `Senpy: A Pragmatic Linked Sentiment Analysis Framework <http://gsi.dit.upm.es/index.php/es/investigacion/publicaciones?view=publication&task=show&id=417>`__ (`BibTex <http://gsi.dit.upm.es/index.php/es/investigacion/publicaciones?controller=publications&task=export&format=bibtex&id=417>`__):
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.. code-block:: text
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Sánchez-Rada, J. F., Iglesias, C. A., Corcuera, I., & Araque, Ó. (2016, October).
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Senpy: A Pragmatic Linked Sentiment Analysis Framework.
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In Data Science and Advanced Analytics (DSAA),
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2016 IEEE International Conference on (pp. 735-742). IEEE.
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Schema Examples
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===============
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Schema
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------
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All the examples in this page use the :download:`the main schema <_static/schemas/definitions.json>`.
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Simple NIF annotation
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---------------------
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.....................
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Description
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...........
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,,,,,,,,,,,
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This example covers the basic example in the NIF documentation: `<http://persistence.uni-leipzig.org/nlp2rdf/ontologies/nif-core/nif-core.html>`_.
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Representation
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..............
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.. literalinclude:: examples/example-basic.json
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,,,,,,,,,,,,,,
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.. literalinclude:: examples/results/example-basic.json
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:language: json-ld
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Sentiment Analysis
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---------------------
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.....................
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Description
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...........
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,,,,,,,,,,,
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Representation
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..............
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,,,,,,,,,,,,,,
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.. literalinclude:: examples/example-sentiment.json
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.. literalinclude:: examples/results/example-sentiment.json
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:emphasize-lines: 5-10,25-33
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:language: json-ld
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Suggestion Mining
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-----------------
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.................
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Description
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...........
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,,,,,,,,,,,
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Representation
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..............
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,,,,,,,,,,,,,,
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.. literalinclude:: examples/example-suggestion.json
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.. literalinclude:: examples/results/example-suggestion.json
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:emphasize-lines: 5-8,22-27
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:language: json-ld
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Emotion Analysis
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----------------
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................
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Description
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...........
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,,,,,,,,,,,
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Representation
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..............
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,,,,,,,,,,,,,,
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.. literalinclude:: examples/example-emotion.json
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.. literalinclude:: examples/results/example-emotion.json
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:language: json-ld
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:emphasize-lines: 5-8,25-37
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Named Entity Recognition
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------------------------
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........................
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Description
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...........
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,,,,,,,,,,,
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Representation
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..............
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,,,,,,,,,,,,,,
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.. literalinclude:: examples/example-ner.json
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.. literalinclude:: examples/results/example-ner.json
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:emphasize-lines: 5-8,19-34
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:language: json-ld
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Complete example
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----------------
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................
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Description
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...........
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,,,,,,,,,,,
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This example covers all of the above cases, integrating all the annotations in the same document.
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Representation
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..............
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,,,,,,,,,,,,,,
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.. literalinclude:: examples/example-complete.json
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.. literalinclude:: examples/results/example-complete.json
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:language: json-ld
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What is Senpy?
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--------------
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Senpy is a framework to build semantic sentiment and emotion analysis services. Using Senpy you can easy develop and publish your own analysis algorithms.
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Senpy is a framework that turns your sentiment or emotion analysis algorithm into a full blown semantic service.
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Senpy takes care of:
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If you use Senpy, please cite `Senpy: A Pragmatic Linked Sentiment Analysis Framework <http://gsi.dit.upm.es/index.php/es/investigacion/publicaciones?view=publication&task=show&id=417>`__ (`BibTex <http://gsi.dit.upm.es/index.php/es/investigacion/publicaciones?controller=publications&task=export&format=bibtex&id=417>`__):
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* Interfacing with the user: parameter validation, error handling.
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* Formatting: JSON-LD, Turtle/n-triples input and output, or simple text input
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* Linked Data: senpy results are semantically annotated, using a series of well established vocabularies, and sane default URIs.
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* User interface: a web UI where users can explore your service and test different settings
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* A client to interact with the service. Currently only available in Python.
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.. code-block:: text
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Sharing your sentiment analysis with the world has never been easier!
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Senpy for service developers
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============================
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Check out the :doc:`plugins` if you have developed an analysis algorithm (e.g. sentiment analysis) and you want to publish it as a service.
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Senpy for end users
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===================
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All services built using senpy share a common interface.
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This allows users to use them (almost) interchangeably.
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Senpy comes with a :ref:`built-in client`.
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Sánchez-Rada, J. F., Iglesias, C. A., Corcuera, I., & Araque, Ó. (2016, October).
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Senpy: A Pragmatic Linked Sentiment Analysis Framework.
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In Data Science and Advanced Analytics (DSAA),
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2016 IEEE International Conference on (pp. 735-742). IEEE.
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.. toctree::
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specifications
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architecture
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:caption: Interested? Check out senpy's:
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architecture
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|
@ -1,7 +1,7 @@
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Specifications
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==============
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Vocabularies and model
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======================
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The model used in Senpy is based on the following specifications:
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The model used in Senpy is based on the following vocabularies:
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* Marl, a vocabulary designed to annotate and describe subjetive opinions expressed on the web or in information systems.
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* Onyx, which is built one the same principles as Marl to annotate and describe emotions, and provides interoperability with Emotion Markup Language.
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