Abstract
Parliamentary 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.
Highlights
Debate transcripts from legislatures such as the United Kingdom (UK) and European Union (EU) parliaments and the United States (US) Congress, among others, provide access to a wealth of information concerning the opinions and attitudes of politicians and their parties towards arguably the most important topics facing societies and their1 3 Vol.:(0123456789)Journal of Computational Social Science (2020) 3:245–270 citizens, as well as potential insights into the democratic processes that take place in the world’s legislative assemblies.In recent years, these debates have attracted the attention of researchers from diverse fields and research backgrounds
These debates have attracted the attention of researchers from diverse fields and research backgrounds. These include, on one hand, computer scientists working in the field of natural language processing (NLP), who have investigated the application and adaptation to the political sphere of methods developed for sentiment analysis of product reviews and blogs, and who have tackled other related tasks in this domain, such as topic detection
Political and social scientists, traditionally relying on expert coding for the analysis of such transcripts, have increasingly been exploring the idea of viewing ‘text as data’ [23], and using computational methods to investigate the positions taken by debate participants
Summary
Journal of Computational Social Science (2020) 3:245–270 citizens, as well as potential insights into the democratic processes that take place in the world’s legislative assemblies In recent years, these debates have attracted the attention of researchers from diverse fields and research backgrounds. A wide range of approaches to the problem of automatic debate analysis have been adopted, with research on this problem varying widely in its aims and methods Within this body of work, there exist many inconsistencies in the use of terminology, with studies in some cases referring to very similar tasks by different names; while in others, the same term may mean quite different things. There are contrasting approaches to modeling the textual data, the level of granularity of the analyses, and, for both supervised learning methods and the evaluation of other approaches, the acquisition and application of labels used to represent the ground-truth speaker sentiment
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