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

被引:33
作者
Abercrombie, Gavin [1 ]
Batista-Navarro, Riza [1 ]
机构
[1] Univ Manchester, Manchester, Lancs, England
来源
JOURNAL OF COMPUTATIONAL SOCIAL SCIENCE | 2020年 / 3卷 / 01期
关键词
Sentiment analysis; Opinion mining; Text as data; Parliamentary debates; Legislative debates; LANGUAGE; WORDS; VOTES;
D O I
10.1007/s42001-019-00060-w
中图分类号
O1 [数学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 0701 ; 070101 ;
摘要
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.
引用
收藏
页码:245 / 270
页数:26
相关论文
共 81 条
  • [61] Rheault L., 2016, Proceedings of the First Workshop on NLP and Computational Social Science, P92, DOI [DOI 10.18653/V1/W16-5612, 10.18653/ v1/W16-5612]
  • [62] Measuring Emotion in Parliamentary Debates with Automated Textual Analysis
    Rheault, Ludovic
    Beelen, Kaspar
    Cochrane, Christopher
    Hirst, Graeme
    [J]. PLOS ONE, 2016, 11 (12):
  • [63] Richards L., 2015, Handling qualitative data: A practical guide, V3rd
  • [64] More than Bags of Words: Sentiment Analysis with Word Embeddings
    Rudkowsky, Elena
    Haselmayer, Martin
    Wastian, Matthias
    Jenny, Marcelo
    Emrich, Stefan
    Sedlmair, Michael
    [J]. COMMUNICATION METHODS AND MEASURES, 2018, 12 (2-3) : 140 - 157
  • [65] Sakamoto T, 2017, IEEE INT CONF BIG DA, P3104, DOI 10.1109/BigData.2017.8258285
  • [66] Salah Zaher, 2013, Advanced Data Mining and Applications. 9th International Conference, ADMA 2013. Proceedings: LNCS 8346, P13, DOI 10.1007/978-3-642-53914-5_2
  • [67] Salah Zaher, 2014, Ph.D. thesis
  • [68] Estimating Intra-Party Preferences: Comparing Speeches to Votes
    Schwarz, Daniel
    Traber, Denise
    Benoit, Kenneth
    [J]. POLITICAL SCIENCE RESEARCH AND METHODS, 2017, 5 (02) : 379 - 396
  • [69] Seligman ME., 2011, Flourish: A visionary new understanding of happiness and wellbeing
  • [70] Sim Yanchuan, 2013, P 2013 C EMPIRICAL M, P91