Sentiment and position-taking analysis of parliamentary debates: a systematic literature review

Author:

Abercrombie GavinORCID,Batista-Navarro Riza

Abstract

AbstractParliamentary and legislative debate transcripts provide access to information concerning the opinions, positions, and policy preferences of elected politicians. They attract attention from researchers from a wide variety of backgrounds, from political and social sciences to computer science. As a result, the problem of computational sentiment and position-taking analysis has been tackled from different perspectives, using varying approaches and methods, and with relatively little collaboration or cross-pollination of ideas. The existing research is scattered across publications from various fields and venues. In this article, we present the results of a systematic literature review of 61 studies, all of which address the automatic analysis of the sentiment and opinions expressed, and the positions taken by speakers in parliamentary (and other legislative) debates. In this review, we discuss the existing research with regard to the aims and objectives of the researchers who work in this area, the automatic analysis tasks which they undertake, and the approaches and methods which they use. We conclude by summarizing their findings, discussing the challenges of applying computational analysis to parliamentary debates, and suggesting possible avenues for further research.

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences,General Environmental Science

Reference81 articles.

1. Abercrombie, G., & Batista-Navarro, R. (2018). ‘Aye’ or ‘no’? Speech-level sentiment analysis of Hansard UK parliamentary debate transcripts. In: Proceedings of the eleventh international conference on language resources and evaluation (LREC-2018). European Languages Resources Association (ELRA), Miyazaki, Japan. https://www.aclweb.org/anthology/L18-1659.

2. Abercrombie, G., & Batista-Navarro, R.T. (2018). Identifying opinion-topics and polarity of parliamentary debate motions. In: Proceedings of the 9th workshop on computational approaches to subjectivity, sentiment and social media analysis. Association for Computational Linguistics, Brussels, Belgium (pp. 280–285). https://doi.org/10.18653/v1/W18-6241. https://www.aclweb.org/anthology/W18-6241.

3. Ahmadalinezhad, M., & Makrehchi, M. (2018). Detecting agreement and disagreement in political debates. In R. Thomson, C. Dancy, A. Hyder, & H. Bisgin (Eds.), Social, cultural, and behavioral modeling (pp. 54–60). Cham: Springer.

4. Akhmedova, S., Semenkin, E., & Stanovov, V. (2018). Co-operation of biology related algorithms for solving opinion mining problems by using different term weighting schemes. In: K. Madani, D. Peaucelle, O. Gusikhin (Eds.) Informatics in control, automation and robotics: 13th international conference, ICINCO 2016 Lisbon, Portugal, 29-31 July, 2016 (pp. 73–90). Cham: Springer. https://doi.org/10.1007/978-3-319-55011-4_4.

5. Allison, B. (2008). Sentiment detection using lexically-based classifiers. In P. Sojka, A. Horák, I. Kopeček, & K. Pala (Eds.), Text, speech and dialogue (pp. 21–28). Berlin: Springer.

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