Authors:
Panagiotis Gourgaris
;
Andreas Kanavos
;
Christos Makris
and
Georgios Perrakis
Affiliation:
University of Patras, Greece
Keyword(s):
Inference Network, Knowledge Extraction, Opinion Mining, Re-ranking Model, Searching and Browsing, Text Mining, Web Information Filtering and Retrieval.
Related
Ontology
Subjects/Areas/Topics:
Enterprise Information Systems
;
Recommendation Systems
;
Software Agents and Internet Computing
Abstract:
In this paper, we address the problem of entity ranking using opinions expressed in users’ reviews. There is an
abundance of opinions on the web, which includes reviews of products and services. Specifically, we examine
techniques which utilize clustering information, for coping with the obstacle of the entity ranking problem.
Building on this framework, we propose a probabilistic network scheme that employs a topic identification
method so as to modify ranking of results based on user personalization. The contribution lies in the construction
of a probabilistic network which takes as input the belief of the user for each query (initially, all entities
are equivalent) and produces a new ranking for the entities as output. We evaluated our implemented methodology
with experiments with the OpinRank Dataset where we observed an improved retrieval performance to
current re-ranking methods.