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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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Architecture
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============
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The main component of a sentiment analysis service is the algorithm itself. However, for the algorithm to work, it needs to get the appropriate parameters from the user, format the results according to the defined API, interact with the user whn errors occur or more information is needed, etc.
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Senpy proposes a modular and dynamic architecture that allows:
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* Implementing different algorithms in a extensible way, yet offering a common interface.
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* Offering common services that facilitate development, so developers can focus on implementing new and better algorithms.
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The framework consists of two main modules: Senpy core, which is the building block of the service, and Senpy plugins, which consist of the analysis algorithm. The next figure depicts a simplified version of the processes involved in an analysis with the Senpy framework.
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.. image:: senpy-architecture.png
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:width: 100%
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:align: center
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Welcome to Senpy's documentation!
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Welcome to Senpy's documentation!
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=================================
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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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.. 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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senpy
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installation
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installation
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usage
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usage
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api
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apischema
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schema
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plugins
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plugins
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conversion
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conversion
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demo
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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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Schema
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===============
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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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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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Simple NIF annotation
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Description
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Description
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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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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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Representation
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.. literalinclude:: examples/example-basic.json
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.. literalinclude:: examples/results/example-basic.json
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:language: json-ld
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:language: json-ld
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Sentiment Analysis
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Sentiment Analysis
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---------------------
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Description
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Description
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Representation
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Representation
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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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:emphasize-lines: 5-10,25-33
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:language: json-ld
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:language: json-ld
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Suggestion Mining
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Suggestion Mining
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-----------------
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Description
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Description
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Representation
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Representation
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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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:emphasize-lines: 5-8,22-27
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:language: json-ld
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:language: json-ld
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Emotion Analysis
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Emotion Analysis
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----------------
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Description
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Description
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Representation
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Representation
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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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:language: json-ld
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:emphasize-lines: 5-8,25-37
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:emphasize-lines: 5-8,25-37
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Named Entity Recognition
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Named Entity Recognition
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------------------------
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Description
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Description
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Representation
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Representation
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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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:emphasize-lines: 5-8,19-34
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:language: json-ld
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:language: json-ld
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Complete example
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Complete example
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----------------
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Description
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Description
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This example covers all of the above cases, integrating all the annotations in the same document.
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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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Representation
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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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:language: json-ld
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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 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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* NIF 2.0, which defines a semantic format and APO for improving interoperability among natural language processing services
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