Word association norms, mutual information, and lexicography
Computational Linguistics
Measuring praise and criticism: Inference of semantic orientation from association
ACM Transactions on Information Systems (TOIS)
Effects of adjective orientation and gradability on sentence subjectivity
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Determining the semantic orientation of terms through gloss classification
Proceedings of the 14th ACM international conference on Information and knowledge management
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
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
A unified relevance model for opinion retrieval
Proceedings of the 18th ACM conference on Information and knowledge management
Affect analysis of text using fuzzy semantic typing
IEEE Transactions on Fuzzy Systems
Identifying noun product features that imply opinions
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Polarity consistency checking for sentiment dictionaries
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Techniques and applications for sentiment analysis
Communications of the ACM
Customer review summarization approach using Twitter and SentiWordNet
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
| Hi-index | 0.02 |
The Web has plenty of reviews, comments and reports about products, services, government policies, institutions, etc. The opinions expressed in these reviews influence how people regard these entities. For example, a product with consistently good reviews is likely to sell well, while a product with numerous bad reviews is likely to sell poorly. Our aim is to build a sentimental word dictionary, which is larger than existing sentimental word dictionaries and has high accuracy. We introduce rules for deduction, which take words with known polarities as input and produce synsets (a set of synonyms with a definition) with polarities. The synsets with deduced polarities can then be used to further deduce the polarities of other words. Experimental results show that for a given sentimental word dictionary with D words, approximately an additional 50% of D words with polarities can be deduced. An experiment is conducted to find the accuracy of a random sample of the deduced words. It is found that the accuracy is about the same as that of comparing the judgment of one human with that of another.