Foundations of statistical natural language processing
Foundations of statistical natural language processing
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
EMNLP '03 Proceedings of the 2003 conference on Empirical methods in natural language processing
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
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
The utility of linguistic rules in opinion mining
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Opinion Mining and Sentiment Analysis
Foundations and Trends in Information Retrieval
Adding redundant features for CRFs-based sentence sentiment classification
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Identifying expressions of opinion in context
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
Extracting opinion targets in a single- and cross-domain setting with conditional random fields
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Creating subjective and objective sentence classifiers from unannotated texts
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Aspect and sentiment extraction based on information-theoretic co-clustering
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
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We introduce ELS, a new method for entity-level sentiment classification using sequence modeling by Conditional Random Fields (CRF). The CRF is trained to identify the sentiment of each word in a document, which is then used to determine the sentiment for the entity, based on where it appears in the text. Due to its sequential nature, the CRF classifier performs better than the common bag-of-words approaches, especially when we target the local sentiment in small parts of a larger document. Identifying the sentiment about a specific entity, mentioned in a blog post or a larger product review, is a special case of such local sentiment classification. Furthermore, the proposed approach performs well even in short pieces of text, where bag-of-words approaches usually fail, due to the sparseness of the resulting feature vector. We have implemented and tested the proposed method on a publicly available benchmark corpus of short product reviews in English. The results that we present in this paper improve significantly upon published results on the same data, thus confirming our intuition about the approach.