Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
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
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
Sentence level discourse parsing using syntactic and lexical information
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Using automatically labelled examples to classify rhetorical relations: An assessment
Natural Language Engineering
Web opinion mining: how to extract opinions from blogs?
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Genre-based paragraph classification for sentiment analysis
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Developing Japanese WordNet Affect for analyzing emotions
WASSA '11 Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis
WikiSent: weakly supervised sentiment analysis through extractive summarization with wikipedia
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Discourse structure and language technology
Natural Language Engineering
Fuzzy clustering for semi-supervised learning --- case study: construction of an emotion lexicon
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
| Hi-index | 0.00 |
Semantic orientation (SO) for texts is often determined on the basis of the positive or negative polarity, or sentiment, found in the text. Polarity is typically extracted using the positive and negative words in the text, with a particular focus on adjectives, since they convey a high degree of opinion. Not all adjectives are created equal, however. Adjectives found in certain parts of the text, and adjectives that refer to particular aspects of what is being evaluated have more significance for the overall sentiment of the text. To capitalize upon this, we weigh adjectives according to their relevance and create three measures of SO: a baseline SO using all adjectives (no restriction); SO using adjectives found in on-topic sentences as determined by a decision-tree classifier; and SO using adjectives in the nuclei of sentences extracted from a high-level discourse parse of the text. In both cases of restricting adjectives based on relevance, performance is comparable to current results in automated SO extraction. Improvements in the decision classifier and discourse parser will likely cause this result to surpass current benchmarks.