Skip to Main content Skip to Navigation
Conference papers

VODUM: a Topic Model Unifying Viewpoint, Topic and Opinion Discovery

Abstract : The surge of opinionated on-line texts provides a wealth of information that can be exploited to analyze users' viewpoints and opinions on various topics. This article presents VODUM, an unsupervised Topic Model designed to jointly discover viewpoints, topics, and opinions in text. We hypothesize that partitioning topical words and viewpoint-specific opinion words using part-of-speech helps to discriminate and identify viewpoints. Quantitative and qualitative experiments on the Bitterlemons collection show the performance of our model. It outperforms state-of-the-art baselines in generalizing data and identifying viewpoints. This result stresses how important topical and opinion words separation is, and how it impacts the accuracy of viewpoint identification.
Keywords : Opinions Viewpoints
Complete list of metadata

Cited literature [13 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01567069
Contributor : Open Archive Toulouse Archive Ouverte (OATAO) Connect in order to contact the contributor
Submitted on : Friday, July 21, 2017 - 4:09:09 PM
Last modification on : Wednesday, June 1, 2022 - 4:10:46 AM

File

thonet_16879.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01567069, version 1
  • OATAO : 16879

Citation

Thibaut Thonet, Guillaume Cabanac, Mohand Boughanem, Karen Pinel-Sauvagnat. VODUM: a Topic Model Unifying Viewpoint, Topic and Opinion Discovery. 38th European Conference on Information Retrieval (ECIR 2016), Mar 2016, Padua, Italy. pp. 533-545. ⟨hal-01567069⟩

Share

Metrics

Record views

103

Files downloads

238