TY - GEN
T1 - Ratable Aspects over Sentiments
T2 - 14th IEEE International Conference on Data Mining, ICDM 2014
AU - Luo, Wenjuan
AU - Zhuang, Fuzhen
AU - Cheng, Xiaohu
AU - He, Qing
AU - Shi, Zhongzhi
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - Most existing rat able aspect generating methods for aspect mining focus on identifying and rating aspects of reviews with overall ratings, while huge amount of unrated reviews are beyond their ability. This drawback motivates the research problem in this paper: predicting aspect ratings and overall ratings for unrated reviews. To solve this problem, we novelly propose a topic model based on Latent Dirichlet Allocation with indirect supervision. Compared with the previous bag-of-words representation of review documents, we utilize the quad-tuples of (head, modifier, rating, entity) to explicitly model the associations between modifiers and ratings. Specifically, our solution for aspect mining in unrated reviews is decomposed into three steps. Firstly, rat able aspects are generated over sentiments from training reviews with overall ratings. Afterwards, inference of aspect identification and rating for unrated reviews are provided. Finally, overall ratings are predicted for unrated reviews. Under this framework, aspect and sentiment associations are captured in the form of joint probabilities through a generative process. The effectiveness of our approach is testified on a real-world dataset crawled from Trip Advisor http://www.tripadvisor.com/, and extensive experiments show that our method significantly outperforms state-of-the-art methods.
AB - Most existing rat able aspect generating methods for aspect mining focus on identifying and rating aspects of reviews with overall ratings, while huge amount of unrated reviews are beyond their ability. This drawback motivates the research problem in this paper: predicting aspect ratings and overall ratings for unrated reviews. To solve this problem, we novelly propose a topic model based on Latent Dirichlet Allocation with indirect supervision. Compared with the previous bag-of-words representation of review documents, we utilize the quad-tuples of (head, modifier, rating, entity) to explicitly model the associations between modifiers and ratings. Specifically, our solution for aspect mining in unrated reviews is decomposed into three steps. Firstly, rat able aspects are generated over sentiments from training reviews with overall ratings. Afterwards, inference of aspect identification and rating for unrated reviews are provided. Finally, overall ratings are predicted for unrated reviews. Under this framework, aspect and sentiment associations are captured in the form of joint probabilities through a generative process. The effectiveness of our approach is testified on a real-world dataset crawled from Trip Advisor http://www.tripadvisor.com/, and extensive experiments show that our method significantly outperforms state-of-the-art methods.
KW - Aspect Identification
KW - Aspect Rating Prediction
KW - Overall Rating Prediction
UR - https://www.scopus.com/pages/publications/84936942877
U2 - 10.1109/ICDM.2014.14
DO - 10.1109/ICDM.2014.14
M3 - 会议稿件
AN - SCOPUS:84936942877
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 380
EP - 389
BT - Proceedings - 14th IEEE International Conference on Data Mining, ICDM 2014
A2 - Kumar, Ravi
A2 - Toivonen, Hannu
A2 - Pei, Jian
A2 - Zhexue Huang, Joshua
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 December 2014 through 17 December 2014
ER -