Paper Info
Title
Disaggregate Hotel Evaluation by Using Diverse Aspects from User Reviews.
Abstract
Experienced opinions about products and services can guide a potential user for a better purchase decision. Fine-grained aspect level opinions embedded within reviews must be explored to discover experienced users' latent opinion about the aspects (i.e. features of products like cost, value for money, etc.) and their relative importance. In this paper, we present an unsupervised approach for discovering coherent hotel aspects based on the user attention. This model effectively integrates techniques like topic modeling and word embeddings along with the frequent noun-adjective co-occurrence statistics to automatically discover coherent hotel aspects. Further supervised methods are used to understand the user's relative emphasis on the aspects and finally rank the hotels. This method does not assume any predefined seed words and discovers coherent level aspects by directly using user attention and word co-occurrence statistics in addition to topic modeling and word embeddings. The performance evaluation of this method was done by collecting various hotel reviews from multiple travel websites. Results show that the proposed methods improved the baseline performance up to 90%. Hence, the results thus obtained are very promising and indicate that the system is simple, scalable and most of all accurate in ranking hotels based on the latent aspects expressed in the user reviews.
Year
DOI
Venue
2019
10.1109/BIGCOMP.2019.8679332
BigComp
Keywords
Field
DocType
Data mining,Feature extraction,Data models,Adaptation models,Estimation,Predictive models,Coherence
Data modeling,Ranking,Information retrieval,Computer science,Feature extraction,Topic model,Scalability
Conference
ISBN
Citations 
PageRank 
978-1-5386-7789-6
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Bidur Devkota100.34
Kyoung-Sook Kim22414.07
Chenyi Zhuang3455.45
hiroyuki miyazaki4153.84