Article
A credibility and classification-based approach for opinion analysis in social networks
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Document Type
Conference Proceeding
Publication Date
1-1-2016
Abstract
© Springer International Publishing Switzerland 2016. There is an ongoing interest in examining users’ experiences made available through social media. Unfortunately these experiences like reviews on products and/or services are sometimes conflicting and thus, do not help develop a concise opinion on these products and/or services. This paper presents a multi-stage approach that extracts and consolidates reviews after addressing specific issues such as user multiidentity and user limited credibility. A system along with a set of experiments demonstrate the feasibility of the approach.
DOI Link
10.1007/978-3-319-45547-1_24
ISBN
9783319455464
Publisher
Springer Verlag
Disciplines
Keywords
- Credibility,
- Multiidentity,
- Opinion,
- Reputation,
- Social media
Scopus ID
84988646706
Indexed in Scopus
Yes
Open Access
No
https://doi.org/10.1007/978-3-319-45547-1_24
Citation Information
Lobna Azaza, Fatima Zohra Ennaji, Zakaria Maamar, Abdelaziz El Fazziki, et al.. "A credibility and classification-based approach for opinion analysis in social networks" Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 9893 LNCS (2016) p. 303 - 316 ISSN: <a href="https://v2.sherpa.ac.uk/id/publication/issn/0302-9743" target="_blank">0302-9743</a> Available at: http://works.bepress.com/zakaria-maamar/10/