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Please use this identifier to cite or link to this item: http://hdl.handle.net/UCSP/15813
Title: Dictionary-based sentiment analysis applied to a specific domain
Authors: Cruz Quispe, Laura Vanessa
Ochoa Luna, José Eduardo
Roche, Mathieu
Poncelet, Pascal
Keywords: Data mining;Information management;Natural language processing systems;Websites;Automatic approaches;Different domains;NAtural language processing;Sentiment analysis;Sentiment dictionaries;Text mining;Topic-oriented;Web Mining;Big data
Issue Date: 2017
Publisher: Springer Verlag
metadata.dc.relation.uri: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85015214674&doi=10.1007%2f978-3-319-55209-5_5&partnerID=40&md5=fccea3d9e8164fefb7ef2acf0648911c
Abstract: The web and social media have been growing exponentially in recent years. We now have access to documents bearing opinions expressed on a broad range of topics. This constitutes a rich resource for natural language processing tasks, particularly for sentiment analysis. Nevertheless, sentiment analysis is usually difficult because expressed sentiments are usually topic-oriented. In this paper, we propose to automatically construct a sentiment dictionary using relevant terms obtained from web pages for a specific domain. This dictionary is initially built by querying the web with a combination of opinion terms, as well as terms of the domain. In order to select only relevant terms we apply two measures AcroDefMI3 and TrueSkill. Experiments conducted on different domains highlight that our automatic approach performs better for specific cases. © Springer International Publishing AG 2017.
URI: http://repositorio.ucsp.edu.pe/handle/UCSP/15813
ISSN: 18650929
Appears in Collections:Artículos - Ciencia de la computación

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