Paper Info
Title
Opinion-Based Entity Ranking using learning to rank.
Abstract
As social media and e-commerce on the Internet continue to grow, opinions have become one of the most important sources of information for users to base their future decisions on. Unfortunately, the large quantities of opinions make it difficult for an individual to comprehend and evaluate them all in a reasonable amount of time. The users have to read a large number of opinions of different entities before making any decision. Recently a new retrieval task in information retrieval known as Opinion-Based Entity Ranking (OpER) has emerged. OpER directly ranks relevant entities based on how well opinions on them are matched with a user's preferences that are given in the form of queries. With such a capability, users do not need to read a large number of opinions available for the entities. Previous research on OpER does not take into account the importance and subjectivity of query keywords in individual opinions of an entity. Entity relevance scores are computed primarily on the basis of occurrences of query keywords match, by assuming all opinions of an entity as a single field of text. Intuitively, entities that have positive judgments and strong relevance with query keywords should be ranked higher than those entities that have poor relevance and negative judgments. This paper outlines several ranking features and develops an intuitive framework for OpER in which entities are ranked according to how well individual opinions of entities are matched with the user's query keywords. As a useful ranking model may be constructed from many ranking features, we apply learning to rank approach based on genetic programming (GP) to combine features in order to develop an effective retrieval model for OpER task. The proposed approach is evaluated on two collections and is found to be significantly more effective than the standard OpER approach. (C) 2015 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2016
10.1016/j.asoc.2015.10.001
Applied Soft Computing
Keywords
Field
DocType
Entity Ranking,Opinion analysis,Learning to rank,Genetic programming
Learning to rank,Social media,Ranking,Information retrieval,Computer science,Genetic programming,Ranking (information retrieval),Artificial intelligence,Opinion analysis,Machine learning,The Internet
Journal
Volume
Issue
ISSN
38
C
1568-4946
Citations 
PageRank 
References 
2
0.38
40
Authors
3
Name
Order
Citations
PageRank
Shariq Bashir116713.48
Wasif Afzal238830.92
Abdul Rauf Baig312615.82