Extraction of Opposite Sentiments in Classified Free Format Text Reviews

Abstract : Most of the previous approaches in opinion mining focus on the classifications of opinion polarities, positive or negative, expressed in customer reviews. In this paper, we present the problem of extracting contextual opposite sentiments in classified free format text reviews. We adapt the sequence data model to text mining with Part-of-Speech tags, and then we propose a belief-driven approach for extracting contextual opposite sentiments as unexpected sequences with respect to the opinion polarity of reviews. We conclude by detailing our experimental results on free format text movie review data.
Document type :
Conference papers
Complete list of metadatas

Cited literature [11 references]  Display  Hide  Download

https://hal-lirmm.ccsd.cnrs.fr/lirmm-00324582
Contributor : Haoyuan Li <>
Submitted on : Thursday, April 2, 2009 - 2:42:17 PM
Last modification on : Wednesday, September 18, 2019 - 11:00:29 AM
Long-term archiving on : Friday, June 4, 2010 - 11:47:32 AM

File

OppositeSentiments.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Haoyuan Li, Anne Laurent, Mathieu Roche, Pascal Poncelet. Extraction of Opposite Sentiments in Classified Free Format Text Reviews. DEXA: Database and Expert Systems Applications, Sep 2008, Turin, Italy. pp.710-717, ⟨10.1007/978-3-540-85654-2_62⟩. ⟨lirmm-00324582⟩

Share

Metrics

Record views

225

Files downloads

183