Dictionary-Based Sentiment Analysis Applied to a Specific Domain

Laura Cruz 1 José Ochoa 2 Mathieu Roche 3 Pascal Poncelet 3
1 UNSA - Universidad Nacional de San Agustín
2 UCSP - Universidad Católica San Pablo
3 ADVANSE - ADVanced Analytics for data SciencE
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
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.
Keywords : Text Mining Web Mining Sentiment analysis
Document type :
Conference papers
Complete list of metadatas
https://hal-lirmm.ccsd.cnrs.fr/lirmm-01910683
Contributor : Pascal Poncelet <>
Submitted on : Thursday, November 1, 2018 - 5:52:11 PM
Last modification on : Monday, November 19, 2018 - 7:56:53 PM
Long-term archiving on : Saturday, February 2, 2019 - 2:00:08 PM

File

37.pdf
Files produced by the author(s)

Identifiers

Collections

LIRMM | ADVANSE | MIPS | UNIV-MONTPELLIER

Citation

Laura Cruz, José Ochoa, Mathieu Roche, Pascal Poncelet. Dictionary-Based Sentiment Analysis Applied to a Specific Domain. SIMBIg: Symposium on Information Management and Big Data, Sep 2016, Cusco, Peru. pp.57-68, ⟨10.1007/978-3-319-55209-5_5⟩. ⟨lirmm-01910683⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

92

Files downloads

71

Contact

support.ccsd.cnrs.fr
hal.support@ccsd.cnrs.fr
Logo CCSD