Senpy: A framework for semantic sentiment and emotion analysis services

Sánchez Rada, Juan Fernando and Araque Iborra, Oscar and Iglesias Fernández, Carlos Ángel (2019). Senpy: A framework for semantic sentiment and emotion analysis services. "Knowledge-Based Systems", v. 190 ; pp. 1-6. ISSN 0950-7051. https://doi.org/10.1016/j.knosys.2019.105193.

Description

Title: Senpy: A framework for semantic sentiment and emotion analysis services
Author/s:
  • Sánchez Rada, Juan Fernando
  • Araque Iborra, Oscar
  • Iglesias Fernández, Carlos Ángel
Item Type: Article
Título de Revista/Publicación: Knowledge-Based Systems
Date: 29 February 2019
ISSN: 0950-7051
Volume: 190
Subjects:
Freetext Keywords: Sentiment Analysis; Emotion Analysis; Linked Data; Classification; Evaluation
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería de Sistemas Telemáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Senpy is a framework to develop, evaluate and publish web services for sentiment and emotion analysis in text. The framework is aimed towards both developers and users. For developers, it is a means to evaluate their classifiers and easily publish them as web services. For users, it is a way to consume sentiment analysis from different providers through the same interface. This is achieved through a combination of an API aligned with the NLP Interchange Format (NIF) service specification, the use of semantic formats and a series of well established vocabularies. The framework is Open Source, and has been used extensively in several projects. As a result, several Senpy Open Source services are available for use and download.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainTEC2015-68284-RSEMOLACarlos A. Iglesias; Tomás RoblesTecnologías de Análisis de Sentimientos y emociones para agentes sociales empáticos en inteligencia ambiental

More information

Item ID: 63653
DC Identifier: http://oa.upm.es/63653/
OAI Identifier: oai:oa.upm.es:63653
DOI: 10.1016/j.knosys.2019.105193
Official URL: https://www.sciencedirect.com/science/article/pii/S0950705119305313?via%3Dihub
Deposited by: Memoria Investigacion
Deposited on: 30 Nov 2020 15:22
Last Modified: 30 Nov 2020 15:22
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