Learning extraction patterns for subjective expressions
EMNLP '03 Proceedings of the 2003 conference on 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
Multilingual subjectivity analysis using machine translation
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Opinion Mining on Newspaper Quotations
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Extracting opinions, opinion holders, and topics expressed in online news media text
SST '06 Proceedings of the Workshop on Sentiment and Subjectivity in Text
Co-training for cross-lingual sentiment classification
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Multilingual subjectivity: are more languages better?
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics
Predicting consumer sentiments from online text
Decision Support Systems
Combining supervised and unsupervised polarity classification for non-english reviews
CICLing'13 Proceedings of the 14th international conference on Computational Linguistics and Intelligent Text Processing - Volume 2
Cross-lingual polarity detection with machine translation
Proceedings of the Second International Workshop on Issues of Sentiment Discovery and Opinion Mining
SAMAR: Subjectivity and sentiment analysis for Arabic social media
Computer Speech and Language
| Hi-index | 0.00 |
The paper presents a semi-automatic approach to creating sentiment dictionaries in many languages. We first produced high-level gold-standard sentiment dictionaries for two languages and then translated them automatically into third languages. Those words that can be found in both target language word lists are likely to be useful because their word senses are likely to be similar to that of the two source languages. These dictionaries can be further corrected, extended and improved. In this paper, we present results that verify our triangulation hypothesis, by evaluating triangulated lists and comparing them to non-triangulated machine-translated word lists.