TY - GEN
T1 - Data-driven contextual valence shifter quantification for multi-theme sentiment analysis
AU - Yu, Hongkun
AU - Shang, Jingbo
AU - Hsu, Meichun
AU - Castellanos, Malú
AU - Han, Jiawei
N1 - Funding Information:
Research was sponsored in part by HP Labs and HPE Vertica, the U.S. Army Research Lab. under Cooperative Agreement No. W911NF-09-2-0053 (NSCTA), National Science Foundation IIS-1320617, IIS-1354329 and IIS 16-18481, HDTRA1-10-1-0120, and grant 1U54GM114838 awarded by NIGMS through funds provided by the trans-NIH Big Data to Knowledge (BD2K) initiative (www.bd2k.nih.gov).
Publisher Copyright:
© 2016 ACM.
PY - 2016/10/24
Y1 - 2016/10/24
N2 - Users often write reviews on different themes involving linguistic structures with complex sentiments. The sentiment polarity of a word can be different across themes. Moreover, contextual valence shifters may change sentiment polarity depending on the contexts that they appear in. Both challenges cannot be modeled effectively and explicitly in traditional sentiment analysis. Studying both phenomena requires multi-theme sentiment analysis at the word level, which is very interesting but significantly more challenging than overall polarity classification. To simultaneously resolve the multi-theme and sentiment shifting problems, we propose a data-driven framework to enable both capabilities: (1) polarity predictions of the same word in reviews of different themes, and (2) discovery and quantification of contextual valence shifters. The framework formulates multi-theme sentiment by factorizing the review sentiments with theme/word embeddings and then derives the shifter effect learning problem as a logistic regression. The improvement of sentiment polarity classification accuracy demonstrates not only the importance of multi-theme and sentiment shifting, but also effectiveness of our framework. Human evaluations and case studies further show the success of multi-theme word sentiment predictions and automatic effect quantification of contextual valence shifters.
AB - Users often write reviews on different themes involving linguistic structures with complex sentiments. The sentiment polarity of a word can be different across themes. Moreover, contextual valence shifters may change sentiment polarity depending on the contexts that they appear in. Both challenges cannot be modeled effectively and explicitly in traditional sentiment analysis. Studying both phenomena requires multi-theme sentiment analysis at the word level, which is very interesting but significantly more challenging than overall polarity classification. To simultaneously resolve the multi-theme and sentiment shifting problems, we propose a data-driven framework to enable both capabilities: (1) polarity predictions of the same word in reviews of different themes, and (2) discovery and quantification of contextual valence shifters. The framework formulates multi-theme sentiment by factorizing the review sentiments with theme/word embeddings and then derives the shifter effect learning problem as a logistic regression. The improvement of sentiment polarity classification accuracy demonstrates not only the importance of multi-theme and sentiment shifting, but also effectiveness of our framework. Human evaluations and case studies further show the success of multi-theme word sentiment predictions and automatic effect quantification of contextual valence shifters.
KW - Multi-theme
KW - Sentiment analysis
KW - Sentiment shifting
UR - http://www.scopus.com/inward/record.url?scp=84996590226&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84996590226&partnerID=8YFLogxK
U2 - 10.1145/2983323.2983793
DO - 10.1145/2983323.2983793
M3 - Conference contribution
AN - SCOPUS:84996590226
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 939
EP - 948
BT - CIKM 2016 - Proceedings of the 2016 ACM Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Y2 - 24 October 2016 through 28 October 2016
ER -