* Merge branch '44-add-basic-evaluation-with-gsitk'
* Refactor requirements (add extra-requirements)
* Skip evaluation tests in Py2
* Fix installation with PIP
* Implement the evaluation service inside the Senpy API
* Connect Plugins to GSITK's evaluation module
* Add an evaluation method inside the Senpy Context
* Add the evaluation models and schemas
* Add Evaluation to the Playground, with a table view
* Add evaluation tests
Web services can get really complex: data validation, user interaction, formatting, logging., etc.
The figure below summarizes the typical features in an analysis service.
Senpy is a framework for text analysis using Linked Data. There are three main applications of Senpy so far: sentiment and emotion analysis, user profiling and entity recoginition. Annotations and Services are compliant with NIF (NLP Interchange Format).
Senpy aims at providing a framework where analysis modules can be integrated easily as plugins, and providing a core functionality for managing tasks such as data validation, user interaction, formatting, logging, translation to linked data, etc.
The figure below summarizes the typical features in a text analysis service.
Senpy implements all the common blocks, so developers can focus on what really matters: great analysis algorithms that solve real problems.
The model used in Senpy is based on the following vocabularies:
The model used in Senpy is based on NIF 2.0 [1], which defines a semantic format and API for improving interoperability among natural language processing services.
* Marl, a vocabulary designed to annotate and describe subjetive opinions expressed on the web or in information systems.
* Onyx, which is built one the same principles as Marl to annotate and describe emotions, and provides interoperability with Emotion Markup Language.
* NIF 2.0, which defines a semantic format and APO for improving interoperability among natural language processing services
Senpy has been applied to sentiment and emotion analysis services using the following vocabularies:
* Marl [2,6], a vocabulary designed to annotate and describe subjetive opinions expressed on the web or in information systems.
* Onyx [3,5], which is built one the same principles as Marl to annotate and describe emotions, and provides interoperability with Emotion Markup Language.
An overview of the vocabularies and their use can be found in [4].
[1] Guidelines for developing NIF-based NLP services, Final Community Group Report 22 December 2015 Available at: https://www.w3.org/2015/09/bpmlod-reports/nif-based-nlp-webservices/
[2] Marl Ontology Specification, available at http://www.gsi.dit.upm.es/ontologies/marl/
[3] Onyx Ontology Specification, available at http://www.gsi.dit.upm.es/ontologies/onyx/
[4] Iglesias, C. A., Sánchez-Rada, J. F., Vulcu, G., & Buitelaar, P. (2017). Linked Data Models for Sentiment and Emotion Analysis in Social Networks. In Sentiment Analysis in Social Networks (pp. 49-69).
[5] Sánchez-Rada, J. F., & Iglesias, C. A. (2016). Onyx: A linked data approach to emotion representation. Information Processing & Management, 52(1), 99-114.
[6] Westerski, A., Iglesias Fernandez, C. A., & Tapia Rico, F. (2011). Linked opinions: Describing sentiments on the structured web of data.