Communications of the ACM
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Information extraction from research papers using conditional random fields
Information Processing and Management: an International Journal
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Identifying sources of opinions with conditional random fields and extraction patterns
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
Extracting opinions, opinion holders, and topics expressed in online news media text
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Sentence and expression level annotation of opinions in user-generated discourse
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
ELS: a word-level method for entity-level sentiment analysis
Proceedings of the International Conference on Web Intelligence, Mining and Semantics
Comparison of feature-level learning methods for mining online consumer reviews
Expert Systems with Applications: An International Journal
Grammatical structures for word-level sentiment detection
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Aspect extraction through semi-supervised modeling
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Cross-domain co-extraction of sentiment and topic lexicons
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Extracting opinion expressions with semi-Markov conditional random fields
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
One seed to find them all: mining opinion features via association
Proceedings of the 21st ACM international conference on Information and knowledge management
Techniques and applications for sentiment analysis
Communications of the ACM
Event argument extraction based on CRF
CLSW'12 Proceedings of the 13th Chinese conference on Chinese Lexical Semantics
Rule-based opinion target and aspect extraction to acquire affective knowledge
Proceedings of the 22nd international conference on World Wide Web companion
Aspect-specific polarity-aware summarization of online reviews
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
SAMAR: Subjectivity and sentiment analysis for Arabic social media
Computer Speech and Language
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In this paper, we focus on the opinion target extraction as part of the opinion mining task. We model the problem as an information extraction task, which we address based on Conditional Random Fields (CRF). As a baseline we employ the supervised algorithm by Zhuang et al. (2006), which represents the state-of-the-art on the employed data. We evaluate the algorithms comprehensively on datasets from four different domains annotated with individual opinion target instances on a sentence level. Furthermore, we investigate the performance of our CRF-based approach and the baseline in a single- and cross-domain opinion target extraction setting. Our CRF-based approach improves the performance by 0.077, 0.126, 0.071 and 0.178 regarding F-Measure in the single-domain extraction in the four domains. In the cross-domain setting our approach improves the performance by 0.409, 0.242, 0.294 and 0.343 regarding F-Measure over the baseline.