Skip to content
This repository was archived by the owner on May 14, 2024. It is now read-only.
/ MRR Public archive

MRR is an unsupervised algorithm based on the concept of graph centrality for identifying relevant documents!

Notifications You must be signed in to change notification settings

vwoloszyn/MRR

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

97 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

MRR

Author: Vinicius Woloszyn, Henrique D. P. dos Santos, Leandro Krug Wives, and Karin Becker

Abstract: The automatic detection of relevant reviews plays a major role in tasks such as opinion summarization, opinion-based recommendation, and opinion retrieval. Supervised approaches for ranking reviews by relevance rely on the existence of a significant, domain-dependent training data set. In this work, we propose MRR (Most Relevant Reviews), a new unsupervised algorithm that identifies relevant revisions based on the concept of graph centrality. The intuition behind MRR is that central reviews highlight aspects of a product that many other reviews frequently mention, with similar opinions, as expressed in terms of ratings. MRR constructs a graph where nodes represent reviews, which are connected by edges when a minimum similarity between a pair of reviews is observed, and then employs PageRank to compute the centrality. The minimum similarity is graph-specific, and takes into account how reviews are written in specific domains. The similarity function does not require extensive pre-processing, thus reducing the computational cost.
Using reviews from books and electronics products, our approach has outperformed the two unsupervised baselines and shown a comparable performance with two supervised regression models in a specific setting. MRR has also achieved a significantly superior run-time performance in a comparison with the unsupervised baselines.

Keywords: opinion retrieval, unsupervised algorithm, relevant reviews.

Full text , Slides

Complete Reference: Vinicius Woloszyn, Henrique D. P. dos Santos, Leandro Krug Wives, and Karin Becker. 2017. MRR: an Unsupervised Algorithm to Rank Reviews by Rel-evance. InProceedings of WI ’17, Leipzig, Germany, August 23-26, 2017,7 pages.DOI: 10.1145/3106426.3106444

Bibtex

About

MRR is an unsupervised algorithm based on the concept of graph centrality for identifying relevant documents!

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Contributors 3

  •  
  •  
  •