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

## TL;DR

This paper systematically reviews 61 studies on automatic sentiment and position analysis in parliamentary debates, highlighting diverse approaches, challenges, and future research directions across multiple disciplines.

## Contribution

It provides a comprehensive overview of existing methods, aims, and challenges in computational analysis of parliamentary debates, fostering cross-disciplinary understanding.

## Key findings

- Research is scattered across disciplines and venues.
- Various approaches and methods are used for sentiment and position analysis.
- Challenges include data complexity and interdisciplinary integration.

## 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 automatic 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 positions taken by speakers in parliamentary (and other legislative) debates. In this review, we discuss the available research with regard to the aims and objectives of the researchers who work on these problems, the automatic analysis tasks they undertake, and the approaches and methods 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.

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Source: https://tomesphere.com/paper/1907.04126