Yejin Choi
ABSTRACT
Determining the polarity of a sentiment-bearing expression requires more than a simple bag-of-words approach. In particular, words or constituents within the expression can interact with each other to yield a particular overall polarity. In this paper, we view such subsentential interactions in light of compositional semantics, and present a novel learning-based approach that incorporates structural inference motivated by compositional semantics into the learning procedure. Our experiments show that (1) simple heuristics based on compositional semantics can perform better than learning-based methods that do not incorporate compositional semantics (accuracy of 89.7% vs. 89.1%), but (2) a method that integrates compositional semantics into learning performs better than all other alternatives (90.7%). We also find that "content-word negators", not widely employed in previous work, play an important role in determining expression-level polarity. Finally, in contrast to conventional wisdom, we find that expression-level classification accuracy uniformly decreases as additional, potentially disambiguating, context is considered.
- Steven Abney. 1996. Partial parsing via finite-state cascades. Journal of Natural Language Engineering, 2(4):337344. Google Scholar
Digital Library
- Koby Crammer and Yoram Singer. 2003. Ultraconservative online algorithms for multiclass problems. JMLR 3:951. Google ScholarDigital Library
- David R. Dowty, Robert E. Wall and Stanley Peters. 1981. Introduction to Montague Semantics.Google Scholar
- Andrea Esuli and Fabrizio Sebastiani. 2006. SentiWordNet: A Publicly Available Lexical Resource for Opinion Mining. In Proceedings of 5th Conference on Language Resources and Evaluation (LREC),.Google Scholar
- Percy Liang, Hal Daumé III and Dan Klein. 2008. Structure Compilation: Trading Structure for Features. In International Conference on Machine Learning. Google ScholarDigital Library
- Minqing Hu and Bing Liu. 2004. Mining and summarizing customer reviews. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery&Data Mining (KDD-2004). Google ScholarDigital Library
- Alistair Kennedy and Diana Inkpen. 2005. Sentiment Classification of Movie and Product Reviews Using Contextual Valence Shifters. In Proceedings of FINEXIN 2005, Workshop on the Analysis of Informal and Formal Information Exchange during Negotiations.Google Scholar
- Soo-Min Kim and Eduard Hovy. 2004. Determining the sentiment of opinions. In Proceedings of COLING. Google ScholarDigital Library
- Ryan McDonald, Kerry Hannan, Tyler Neylon, Mike Wells and Jeff Reynar. 2007. Structured Models for Fine-to-Coarse Sentiment Analysis. In Proceedings of Association for Computational Linguistics (ACL).Google Scholar
- George A. Miller. 1995. WordNet: a lexical database for English. In Communications of the ACM, 38(11):3941 Google ScholarDigital Library
- Richard Montague. 1974. Formal Philosophy; Selected papers of Richard Montague. Yale University Press.Google Scholar
- Karo Moilanen and Stephen Pulman. 2007. Sentiment Composition. In Proceedings of Recent Advances in Natural Language Processing (RANLP 2007).Google Scholar
- Rowan Nairn, Cleo Condoravdi and Lauri Karttunen 2006. Computing relative polarity for textual inference. In Inference in Computational Semantics (ICoS-5).Google Scholar
- Yun Niu, Xiaodan Zhu, Jianhua Li and Graeme Hirst. 2005. Analysis of polarity information in medical text. In Proceedings of the American Medical Informatics Association 2005 Annual Symposium (AMIA).Google Scholar
- Livia Polanyi and Annie Zaenen. 2004. Contextual lexical valence shifters. In Exploring Attitude and Affect in Text: Theories and Applications: Papers from the 2004 Spring Symposium, AAAI.Google Scholar
- Mostafa Shaikh, Helmut Prendinger and Mitsuru Ishizuka. 2007. Assessing sentiment of text by semantic dependency and contextual valence analysis. In Proc 2nd Int'l Conf on Affective Computing and Intelligent Interaction (ACII'07). Google ScholarDigital Library
- Carlo Strapparava and Rada Mihalcea. 2007. Semeval-2007 task 14: Affective text. In Proceedings of SemEval. Google ScholarDigital Library
- Janyce Wiebe, Theresa Wilson and Claire Cardie. 2005. Annotating expressions of opinions and emotions in language. In Language Resources and Evaluation (formerly Computers and the Humanities), 39(2-3):165210.Google Scholar
- Theresa Wilson, Janyce Wiebe and Paul Hoffmann. 2005. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of HLT/EMNLP. Google ScholarDigital Library
Recommendations
Contextual semantics for sentiment analysis of Twitter
We propose a semantic sentiment representation of words called SentiCircle.SentiCircle captures the contextual semantic of words from their co-occurrences.SentiCircle updates the sentiment of words based on their contextual semantics.SentiCircle can be ...
Joint sentiment/topic model for sentiment analysis
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge managementSentiment analysis or opinion mining aims to use automated tools to detect subjective information such as opinions, attitudes, and feelings expressed in text. This paper proposes a novel probabilistic modeling framework based on Latent Dirichlet ...
Topic sentiment change analysis
MLDM'11: Proceedings of the 7th international conference on Machine learning and data mining in pattern recognitionPublic opinions on a topic may change over time. Topic Sentiment change analysis is a new research problem consisting of two main components: (a) mining opinions on a certain topic, and (b) detect significant changes of sentiment of the opinions on the ...
Comments