A limited memory algorithm for bound constrained optimization
SIAM Journal on Scientific Computing
Learning Subjective Adjectives from Corpora
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Co-clustering Documents and Words Using Bipartite Spectral GraphPartitioning
Co-clustering Documents and Words Using Bipartite Spectral GraphPartitioning
Predicting the semantic orientation of adjectives
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Recognizing contextual polarity in phrase-level sentiment analysis
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Semi-supervised polarity lexicon induction
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Learning with compositional semantics as structural inference for subsentential sentiment analysis
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
TextGraphs-1 Proceedings of the First Workshop on Graph Based Methods for Natural Language Processing
Review sentiment scoring via a parse-and-paraphrase paradigm
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Generating high-coverage semantic orientation lexicons from overtly marked words and a thesaurus
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
The viability of web-derived polarity lexicons
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Dependency tree-based sentiment classification using CRFs with hidden variables
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
"Was it good? It was provocative." Learning the meaning of scalar adjectives
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Compositional matrix-space models of language
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
NeSp-NLP '10 Proceedings of the Workshop on Negation and Speculation in Natural Language Processing
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Lexicon-based methods for sentiment analysis
Computational Linguistics
Evaluating distributional models of semantics for syntactically invariant inference
EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Semantic compositionality through recursive matrix-vector spaces
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
How do negation and modality impact on opinions?
ExProM '12 Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics
| Hi-index | 0.00 |
We present a general learning-based approach for phrase-level sentiment analysis that adopts an ordinal sentiment scale and is explicitly compositional in nature. Thus, we can model the compositional effects required for accurate assignment of phrase-level sentiment. For example, combining an adverb (e.g., "very") with a positive polar adjective (e.g., "good") produces a phrase ("very good") with increased polarity over the adjective alone. Inspired by recent work on distributional approaches to compositionality, we model each word as a matrix and combine words using iterated matrix multiplication, which allows for the modeling of both additive and multiplicative semantic effects. Although the multiplication-based matrix-space framework has been shown to be a theoretically elegant way to model composition (Rudolph and Giesbrecht, 2010), training such models has to be done carefully: the optimization is non-convex and requires a good initial starting point. This paper presents the first such algorithm for learning a matrix-space model for semantic composition. In the context of the phrase-level sentiment analysis task, our experimental results show statistically significant improvements in performance over a bag-of-words model.