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
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
Label propagation through linear neighborhoods
ICML '06 Proceedings of the 23rd international conference on Machine learning
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
Semi-supervised polarity lexicon induction
EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
Fully automatic lexicon expansion for domain-oriented sentiment analysis
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Expanding domain sentiment lexicon through double propagation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Phrase dependency parsing for opinion mining
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3 - Volume 3
Constrained spectral clustering via exhaustive and efficient constraint propagation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Automatic construction of a context-aware sentiment lexicon: an optimization approach
Proceedings of the 20th international conference on World wide web
Opinion word expansion and target extraction through double propagation
Computational Linguistics
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Using games with a purpose and bootstrapping to create domain-specific sentiment lexicons
Proceedings of the 20th ACM international conference on Information and knowledge management
Leveraging web 2.0 data for scalable semi-supervised learning of domain-specific sentiment lexicons
Proceedings of the 20th ACM international conference on Information and knowledge management
Automatic expansion of feature-level opinion lexicons
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
Information Retrieval
| Hi-index | 0.00 |
Domain-specific sentiment lexicon has played an important role in most practical opinion mining systems. Due to the ubiquitous domain diversity and absence of domain-specific prior knowledge, automatic construction of domain-specific sentiment lexicon has become a challenging research topic in recent years. This paper proposes a novel automatic construction strategy of domain-specific sentiment lexicon based on constrained label propagation. The candidate sentiment terms are extracted by leveraging the chunk dependency information and prior generic lexicon. The pairwise contextual and morphological constraints are defined and extracted between sentiment terms from the domain corpus, and are exploited as prior knowledge to improve the sentiment lexicon construction. The constraint propagation is applied to spread the effect of local constraints throughout the entire collection of candidate sentiment terms. The final propagated constraints are incorporated into the label propagation for the domain-specific sentiment lexicon construction. Experimental results on real-life datasets demonstrate that our approach to constrained label propagation could dramatically improve the performance of automatic construction of domain-specific sentiment lexicon.