TY - GEN
T1 - Jointly modeling multi-grain aspects and opinions for large-scale online review
AU - Zhang, Yang
AU - Tang, Feilong
AU - Barolli, Leonard
AU - Yang, Yanqin
AU - Xu, Wenchao
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/5
Y1 - 2017/5/5
N2 - To aggregate opinions on aspects of entities mentioned in large-scale online reviews, it is important to automatically extract aspects of different granularities, identify associated opinions, especially aspect-specific opinions, and classify sentiment polarity. Recently, various topic models are proposed to process some of these tasks, but there is little work available to do all simultaneously. In this paper, we propose a Joint Aspect-Based Sentiment Topic (JABST) model to jointly extracting multi-grain aspects and opinions, which addresses all the tasks mentioned above. JABST models aspect, opinion, sentiment polarity and granularity simultaneously. To better separate opinion and aspect words, we propose JABST-ME, in which a maximum entropy (ME) classifier is applied to extend JABST. We evaluated the models on reviews of electronic devices and restaurants qualitatively and quantitatively. The experimental results show that the proposed models outperform state-of-the-art baselines.
AB - To aggregate opinions on aspects of entities mentioned in large-scale online reviews, it is important to automatically extract aspects of different granularities, identify associated opinions, especially aspect-specific opinions, and classify sentiment polarity. Recently, various topic models are proposed to process some of these tasks, but there is little work available to do all simultaneously. In this paper, we propose a Joint Aspect-Based Sentiment Topic (JABST) model to jointly extracting multi-grain aspects and opinions, which addresses all the tasks mentioned above. JABST models aspect, opinion, sentiment polarity and granularity simultaneously. To better separate opinion and aspect words, we propose JABST-ME, in which a maximum entropy (ME) classifier is applied to extend JABST. We evaluated the models on reviews of electronic devices and restaurants qualitatively and quantitatively. The experimental results show that the proposed models outperform state-of-the-art baselines.
KW - Aspect-specific opinions
KW - Multi-grain aspects
KW - Sentiment analysis
KW - Topic model
UR - https://www.scopus.com/pages/publications/85019674527
U2 - 10.1109/AINA.2017.122
DO - 10.1109/AINA.2017.122
M3 - 会议稿件
AN - SCOPUS:85019674527
T3 - Proceedings - International Conference on Advanced Information Networking and Applications, AINA
SP - 570
EP - 577
BT - Proceedings - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017
A2 - Enokido, Tomoya
A2 - Hsu, Hui-Huang
A2 - Lin, Chi-Yi
A2 - Takizawa, Makoto
A2 - Barolli, Leonard
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 31st IEEE International Conference on Advanced Information Networking and Applications, AINA 2017
Y2 - 27 March 2017 through 29 March 2017
ER -