Authors:
Youssef Meguebli
1
;
Mouna Kacimi
2
;
Bich-liên Doan
1
and
Fabrice Popineau
1
Affiliations:
1
SUPELEC Systems Sciences (E3S), France
;
2
Free University of Bozen-Bolzano, Italy
Keyword(s):
Opinion Ranking, Opinion Mining, Topic Aspects Extraction.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Soft Computing
;
Symbolic Systems
;
Web Mining
Abstract:
The number of opinions in news media platforms is increasing dramatically with daily news hits, and people spending more and more time to discuss topics and share experiences. Such user generated content represents
a promising source for improving the effectiveness of news articles recommendation and retrieval. However, the corpus of opinions is often large and noisy making it hard to find prominent content. In this paper, we tackle
this problem by proposing a novel scoring model that ranks opinions based on their relevance and prominence.
We define the prominence of an opinion using its relationships with other opinions. To this end, we (1) create a directed graph of opinions where each link represents the sentiment an opinion expresses about another opinion (2) propose a new variation of the PageRank algorithm that boosts the scores of opinions along links with positive sentiments and decreases them along links with negative sentiments. We have tested the effectiveness of our model
through extensive experiments using three datasets crawled from CNN, Independent, and The Telegraph Web sites . The experiments show that our scoring model achieves high quality results.
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