Investigating Political Herd Mentality: A Community Sentiment Based Approach

Abstract

Analyzing polarities and sentiments inherent in political speeches and debates poses an important problem today. This experiment aims to address this issue by analyzing publiclyavailable Hansard transcripts of the debates conducted in the UK Parliament. Our proposed approach, which uses community-based graph information to augment hand-crafted features based on topic modeling and emotion detection on debate transcripts currently surpasses the benchmark results on the same dataset. Such sentiment classification systems could prove to be of great use in today's politically turbulent times, for public knowledge of politicians stands on various relevant issues proves vital for good governance and citizenship. The experiments also demonstrate that continuous feature representations learned from graphs can improve performance on sentiment classification tasks significantly.

Analysis of Parliamentary Debate Transcripts Using Community-Based Graphical Approaches (Student Abstract)

“…Compared to the results presented in (Bhavan et al 2019), classification performance was enhanced by using Deep-Walk instead of node2vec and lesser number of textual features (2, compared to 3 used in the baseline paper). The dimensionality of the feature vector is thus reduced to a great extent.…”

“…For the graph-based features, we constructed two graphs namely SimGraph and OppGraph. These graphs are based on those presented in (Bhavan et al 2019).…”

GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion

“…and M., 2018;Duthie and Budzynska, 2018), emotion analysis (Rheault, 2016;Dzieciatko, 2019), topic-opinion analysis (Nguyen et al, 2015;Abercrombie and Batista-Navarro, 2018b) and, debate stance classification (Proksch et al, 2019). Existing work focuses on these tasks through legislative speeches from the US Congress (Chen et al, 2017), the UK Parliament (Bhavan et al, 2019), and the EU Parliament (Glavaš et al, 2017;Frid-Nielsen, 2018) and through social media such as Twitter (Trilling, 2014;Boutyline and Willer, 2017). Recently, some tools for extracting and annotating political data have also been developed (Haddadan et al, 2019).…”

“…They show that more conservative users exhibit a higher level of homophily and often stick to their political stances even when challenged. Recent works (Bhavan et al, 2020;Bhavan et al, 2019) have shown the presence of herd mentality in political stances through graph embeddings by identifying the linguistic similarity between members of the same political party over a set of 1,251 debates. (Davoodi et al, 2020) study the interactions between the content of a proposed bill and the legislative context in which it is presented.…”

“…We also experiment with node2vec from(Bhavan et al, 2019), however, as reported in their following work(Bhavan et al, 2020), there is no significant difference between both, so we only report results based on the more recent method for brevity.…”