TY - CONF
KW - data mining
KW - customer satisfaction
KW - hotel industry
KW - statistical analysis
KW - unsupervised learning
KW - purchase decision
KW - latent aspects
KW - hotel reviews
KW - word co-occurrence statistics
KW - supervised methods
KW - frequent noun-adjective co-occurrence statistics
KW - word embeddings
KW - topic modeling
KW - user attention
KW - coherent hotel aspects
KW - fine-grained aspect level opinions
KW - user reviews
KW - diverse aspects
KW - disaggregate hotel evaluation
KW - Feature extraction
KW - Data models
KW - Adaptation models
KW - Estimation
KW - Predictive models
KW - Coherence
KW - aspects
KW - hotel ranking
AU - B. Devkota
AU - K. Kim
AU - C. Zhuang
AU - H. Miyazaki
AB - 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.
BT - 2019 IEEE International Conference on Big Data and Smart Computing (BigComp)
DA - Feb
DO - 10.1109/BIGCOMP.2019.8679332
LA - eng
N2 - 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.
PY - 2019
T2 - 2019 IEEE International Conference on Big Data and Smart Computing (BigComp)
TI - Disaggregate Hotel Evaluation by Using Diverse Aspects from User Reviews
ER -