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Measuring praise and criticism: Inference of semantic orientation from association
Open accessAbstract
The evaluative character of a word is called its semantic orientation . Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and negative semantic orientation indicates criticism (e.g., "disturbing", "superfluous"). Semantic orientation varies in both direction (positive or negative) and degree (mild to strong). An automated system for measuring semantic orientation would have application in text classification, text filtering, tracking opinions in online discussions, analysis of survey responses, and automated chat systems ( chatbots ). This article introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words. Two instances of this approach are evaluated, based on two different statistical measures of word association: pointwise mutual information (PMI) and latent semantic analysis (LSA). The method is experimentally tested with 3,596 words (including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive (1,614 words) and negative (1,982 words). The method attains an accuracy of 82.8% on the full test set, but the accuracy rises above 95% when the algorithm is allowed to abstain from classifying mild words.
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Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Research works printed but not linked: 28
- Agresti A., An Introduction to Categorical Data Analysis, 10.1002/0470114754 · DOI
- 10.1145/133160.133191 · DOI
- Budanitsky A., Workshop on WordNet and Other Lexical Resources, Second meeting of the North American Chapter of the Association for Computational Linguistics
- 10.3115/981623.981633 · DOI · Paper page
- 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9 · DOI
- 10.5555/972450.972454 · doi:10.5555/972450.972454
- Firth J. R., Studies in Linguistic Analysis
- 10.3115/976909.979640 · DOI · Paper page
- 10.3115/990820.990864 · DOI · Paper page
- Hearst M. A., Text-Based Intelligent Systems: Current Research and Practice in Information Extraction and Retrieval
- Kamps J., Proceedings of the 1st International Conference on Global WordNet. CIIL
- Landauer T. K. 2002. On the computational basis of learning and cognition: Arguments from LSA. In The Psychology of Learning and Motivation B. H. Ross Ed. To appear.]] Landauer T. K. 2002. On the computational basis of learning and cognition: Arguments from LSA. In The Psychology of Learning and Motivation B. H. Ross Ed. To appear.]] · DOI
- 10.1037/0033-295X.104.2.211 · DOI · Paper page
- Manning C. D. and Schütze H. 1999. Foundations of Statistical Natural Language Processing. MIT Press Cambridge Mass.]] Manning C. D. and Schütze H. 1999. Foundations of Statistical Natural Language Processing. MIT Press Cambridge Mass.]]
- 10.1093/ijl/3.4.235 · DOI · Paper page
- Osgood C. E. Suci G. J. and Tannenbaum P. H. 1957. The Measurement of Meaning. University of Illinois Press Chicago Ill.]] Osgood C. E. Suci G. J. and Tannenbaum P. H. 1957. The Measurement of Meaning. University of Illinois Press Chicago Ill.]]
- 10.3115/1118693.1118704 · DOI · Paper page
- Resnik P., 1995, Proceedings of the 14th International Joint Conference on Artificial Intelligence. Morgan-Kaufmann, San Mateo, Calif., 448--453
- Schütze H., Advances in Neural Information Processing Systems 5
- 10.5555/972450.972458 · doi:10.5555/972450.972458
- Spertus E., 1997, Proceedings of the Conference on Innovative Applications of Artificial Intelligence. AAAI Press, Menlo Park, Calif., 1058--1065
- Stone P. J. Dunphy D. C. Smith M. S. and Ogilvie D. M. 1966. The General Inquirer: A Computer Approach to Content Analysis. MIT Press Cambridge Mass.]] Stone P. J. Dunphy D. C. Smith M. S. and Ogilvie D. M. 1966. The General Inquirer: A Computer Approach to Content Analysis. MIT Press Cambridge Mass.]]
- Tong R. M., 2001, Working Notes of the ACM SIGIR 2001 Workshop on Operational Text Classification. ACM
- 10.1007/3-540-44795-4_42 · DOI · Paper page
- 10.3115/1073083.1073153 · DOI · Paper page
- van Rijsbergen C. J., Information Retrieval
- Wiebe J. M., 2000, Proceedings of the 17th National Conference on Artificial Intelligence. AAAI Press, Menlo Park, Calif.]]
- 10.3115/1118078.1118104 · DOI
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Metadata
- Authors
- Turney, P. D., & Littman, M. L.
- Venue
- ACM Transactions on Information Systems
- Published
- 2003-10
- Volume
- 21
- Issue
- 4
- Pages
- 315-346
- ISSN
- 1046-8188 (ISSN-L)
- 1558-2868
- OpenAlex
- W2168625136
- Cited by
- 1,502
- References
- 1
- FWCI
- 25.44
- Type
- journal article
- Language
- en
- Open access
- green
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