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Corpus-based dictionaries for sentiment analysis of specialized vocabularies

Published online by Cambridge University Press:  02 April 2019

Douglas R. Rice  and
Christopher Zorn
Douglas R. Rice*
Affiliation:
Department of Political Science, University of Massachusetts, Amherst, Massachusetts, United States
Christopher Zorn
Affiliation:
Department of Political Science, Pennsylvania State University, University Park, PennsylvaniaUnited States
*
*Corresponding author. Email: [email protected]
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Abstract

Contemporary dictionary-based approaches to sentiment analysis exhibit serious validity problems when applied to specialized vocabularies, but human-coded dictionaries for such applications are often labor-intensive and inefficient to develop. We demonstrate the validity of “minimally-supervised” approaches for the creation of a sentiment dictionary from a corpus of text drawn from a specialized vocabulary. We demonstrate the validity of this approach in estimating sentiment from texts in a large-scale benchmarking dataset recently introduced in computational linguistics, and demonstrate the improvements in accuracy of our approach over well-known standard (nonspecialized) sentiment dictionaries. Finally, we show the usefulness of our approach in an application to the specialized language used in US federal appellate court decisions.

Keywords

Text and content analysis
Type
Original Article
Information
Political Science Research and Methods , Volume 9 , Issue 1 , January 2021 , pp. 20 - 35
Copyright
Copyright © The European Political Science Association 2019

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Footnotes

All materials necessary to replicate the results reported herein are posted to the Political Science Research and Methods Dataverse.

References

Black, R, Treul, S, Johnson, T and Goldman, J (2011) Emotions, oral arguments, and Supreme Court decision making. Journal of Politics 73, 572–581.CrossRefGoogle Scholar
Black, R, Hall, M, Owens, R and Ringsmuth, E (2016) The role of emotional language in briefs before the US Supreme Court. Journal of Law & Courts 4, 377–407.CrossRefGoogle Scholar
Bryan, A and Ringsmuth, E (2016) Jeremiad or weapon of words?: the power of emotive language in Supreme Court dissents. Journal of Law & Courts 4, 159–185.CrossRefGoogle Scholar
Caldeira, G and Zorn, C (1998) Of time and consensual norms in the Supreme Court. American Journal of Political Science 42, 874–902.CrossRefGoogle Scholar
Danelski, D (1960) The influence of the chief justice in the decisional process of the Supreme Court. In Paper Presented at the Annual Meeting of the Midwest Political Science Association, Chicago, Illinois.Google Scholar
Dave, K, Lawrence, S and Pennock, D (2003) Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In 12th International World Wide Web Conference.CrossRefGoogle Scholar
Epstein, L, Landes, W and Posner, R (2011) Why (and when) judges dissent: a theoretical and empirical analysis. Journal of Legal Analysis 3, 101–137.CrossRefGoogle Scholar
Finkelman, P (2006) Biographical Encyclopedia of the Supreme Court: The Lives and Legal, Chapter Roger Brook Taney, Washington, DC: CQ Press, pp. 531–541.Google Scholar
Gerner, D, Schrodt, P, Francisco, R and Weddle, J (1994) The analysis of political events using machine coded data. International Studies Quarterly 38, 91–119.CrossRefGoogle Scholar
Grimmer, J and Stewart, B (2013) Text as data: the promise and pitfalls of automatic content analysis methods for political texts. Political Analysis 21, 267–297.CrossRefGoogle Scholar
Hansen, L, Arvidsson, A, Nielsen, F, Colleoni, E and Etter, M (2011) Good friends, bad news—affect and virality in twitter. In The 2011 International Workshop on Social Computing, Network, and Services (SocialComNet).CrossRefGoogle Scholar
Haynie, S (1992) Leadership and consensus on the U.S. Supreme Court. Journal of Politics 54, 1158–1169.CrossRefGoogle Scholar
Hendershot, M, Hurwitz, M, Lanie, D and Pacelle, R (2013) Dissensual decision making: revisiting the demise of consensual norms with the U.S. Supreme Court. Political Research Quarterly 66, 467–481.CrossRefGoogle Scholar
Liu, B (2010) Sentiment analysis and subjectivity. In Indurkya, N and Damerau, F (eds). Handbook of Natural Language Processing, 2nd Edn. Boca Raton, FL: Chapman and Hall/CRC Press, pp. 627–666.Google Scholar
Maas, A, Daly, R, Pham, P, Huang, D, Ng, A and Potts, C (2011) Learning word vectors for sentiment analysis. In The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011).Google Scholar
Mikolov, T, Chen, K, Corrado, G and Dean, J (2013a) Efficient estimation of word representation in vector space. In ICLR Workshop.Google Scholar
Mikolov, T, Sutskever, I, Chen, K, Corrado, G and Dean, J (2013b) Distributed representation of words and phrases and their compositionality. In NIPS.Google Scholar
Nematzadeh, A, Meylan, S and Griffiths, T (2017) Evaluating vector-space models of word representation, or, the unreasonable effectiveness of counting words near other words. In Proceedings of the 39th Annual Meeting of the Cognitive Science Society.Google Scholar
Nielsen, F (2011) A new anew: evaluation of a word list for sentiment analysis in microblogs. In The ESQ2011 Workshop on “Making Sense of Microposts”.Google Scholar
Pang, B and Lee, L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the Association for Computational Linguistics, pp. 271–278.CrossRefGoogle Scholar
Pang, B, Lee, L and Vaithyanathan, S (2002) Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 79–86.Google Scholar
Pennebaker, J, Francis, M and Booth, R (2001) Linguistic Inquiry and Word Count: LIWC2001. Mahwah, NJ: Erlbaum Publishers.Google Scholar
Pennington, J, Socher, R and Manning, CD (2014) Glove: global vectors for word representation. In Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543.CrossRefGoogle Scholar
Pratt, W (1999) The Supreme Court Under Edward Douglass White, 1910–1921. Columbia, SC: University of South Carolina Press.Google Scholar
Quinn, K, Monroe, B, Crespin, M, Colaresi, M and Radev, D (2010) How to analyze political attention with minimal assumptions and costs. American Journal of Political Science 54, 209–228.CrossRefGoogle Scholar
Rice, D (2017) Issue divisions and U.S. Supreme Court decision making. Journal of Politics 79, 210–222.CrossRefGoogle Scholar
Rise, E (2006) Biographical Encyclopedia of the Supreme Court: The Lives and Legal, Chapter Harold Hitz Burton, Washington, DC: CQ Press, pp. 100–104.Google Scholar
Salamone, M (2013) Judicial consensus and public opinion: conditional response to Supreme Court majority size. Political Research Quarterly 67, 320–334.CrossRefGoogle Scholar
Selivanov, D (2016) text2vec: Modern Text Mining Framework for R. R package version 0.4.0.Google Scholar
Spaeth, HJ, Epstein, L, Ruger, TW, Whittington, KE, Segal, JA and Martin, AD (2012) The Supreme Court database. http://supremecourtdatabase.org.Google Scholar
Stephenson, DG (1973) The chief justice as leader: the case of morrison waite. William and Mary Law Review 14, 899–927.Google Scholar
Tang, D, Wei, F, Yang, N, Zhou, M, Liu, T and Qin, B (2014) Learning sentiment-specific word embedding for twitter sentiment classification. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, Maryland, pp. 1555–1565. Association for Computational Linguistics.CrossRefGoogle Scholar
Tang, D, Wei, F, Qin, B, Yang, N, Liu, T and Zhou, M (2016) Sentiment embeddings with applications to sentiment analysis. Knowledge and Data Engineering, IEEE Transactions on 28, 496–509.CrossRefGoogle Scholar
Turney, P (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In 40th Annual Meeting of the Association for Computational Linguistics, pp. 417–424.Google Scholar
Uszkoreit, H, Xu, F and Li, H (2009) Analysis and improvement of minimally supervised machine learning for relation extraction. In NLDB09 Proceedings of the 14th International Conference on Applications of Natural Language to Information Systems.Google Scholar
Walker, T, Epstein, L and Dixon, W (1988) On the mysterious demise of consensual norms in the United States Supreme Court. Journal of Politics 50, 361–389.CrossRefGoogle Scholar
Wang, P and Domeniconi, C (2008) Building semantic kernels for text classification using wikipedia. In 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 713–721.CrossRefGoogle Scholar
Zink, J, Spriggs, J and Scott, J (2009) Courting the public: the influence of decision attributes on individuals' views of court opinions. Journal of Politics 71, 909–925.CrossRefGoogle Scholar
Supplementary material: Link

Rice and Zorn Dataset

Dataset

https://doi.org/10.7910/DVN/4EKHFM
Link
Supplementary material: PDF

Rice and Zorn supplementary material

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