GP ol S : A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion (2020)

Abstract: Parliamentary debates present a valuable language resource for analyzing comprehensive options in electing representatives under a functional, free society. However, the esoteric nature of political speech coupled with non-linguistic aspects such as political cohesion between party members presents a complex and underexplored task of contextual parliamentary debate analysis. We introduce GPolS, a neural model for political speech sentiment analysis jointly exploiting both semantic language representations and relations between debate transcripts, motions, and political party members. Through experiments on real-world English data and by visualizing attention, we provide a use case of GPolS as a tool for political speech analysis and polarity prediction.

## introduction politics is broadly defined as the set of activities associated with the governance of a country or a region. it involves various aspects that influence critical decisions having national importance. one such aspect is the conduct of parliamentary debates between political parties having ruling and opposition power. these debates discuss matters affecting the future development of a nation, such as economic and societal growth, policy reforms, and budget revisions. records of such debates act as a valuable language resource as they provide a wealth of information regarding viewpoints of political representatives over critical societal factors @xcite , and also for assessing political candidates and basing voting decisions @xcite . analyzing sentiment in such political discourse is propelled by the developments in fields like behavioral economics that bring the psychological aspects of parliamentary decision-making to the forefront @xcite . however, under parliament's rules of behavior, 1 the language used in political debates is complex, laden with domain-specific procedural jargon used in the political realm, and obscure @xcite . this esoteric and tedious nature of political debates makes their analysis complex, forming a barrier to ordinary citizen's insights into political stances and wide-ranging consequences they entail @xcite . the good news is that natural language processing (nlp) shows promise for analyzing voluminous political debates and breaking the understanding barrier towards political ideology to help make informed voting decisions @xcite . however, conventional language models @xcite may not generalize well on understanding the obscure linguistic styles of political debates. this complexity arises due to the lack of political context and procedural parliamentary jargon in generic corpora over which traditional text representation models are trained @xcite . for instance, in the political context, red state is a state that primarily votes for republicans, whereas a blue state votes primarily for democrats. leveraging the success of unsupervised pre-training in nlp @xcite , fine-tuning pre-trained models over the voluminous debate transcripts can lead to drastic advances in analyzing political debates. u 1 u 2 peter aldous richard drax conservative party police grant report i beg to move, that the police grant report (england and wales) for 2018-19 (hc 745), which was laid before this house on 31 january, be approved. u 3 defence reforms i beg to move, this house notes concerns about the government's defence reforms in relation to whether its proposals for the reserve forces will deliver anticipated cost saving savings or defence capability... ## methodology we first define the problem of analyzing parliamentary debates, and then present gpols. figure 2 presents an overview of gpols. we fine-tune and use bert to encode parliamentary debates and motions. we then motivate gpols by analyzing the similarity across debate transcripts based on three types of context and model them through a heterogeneous graph. lastly, we detail the graph attention network (gat) for propagating contextual information across debate transcripts, speakers and motions for classification. problem definition we denote a debate transcript as t @xmath0, t 2 , . . . , t n } corresponding to the speech made by a mp (speaker) s @xmath1, s 2 , . . . , s z } on one specific motion m @xmath2, m 2 , . . . , m q }. each speaker s i is affiliated to only one political party p @xmath3, p 2 , . . . , p r }. on a debate motion m, given a transcript t spoken by a mp s with a political party affiliation p, the task is to classify the stance @xmath4, 'no'} of the mp s on the motion m based on the transcript t. 'aye' and 'no' represent a positive and negative stance on the motion, respectively. ## conclusion we propose gpols, that enhances linguistic analysis of debates with context for political stance detection. fine-tuning bert, we encode debates and motions from the uk parliament's house of commons to capture semantics in political jargon. we show gpols practical applicability and interpretability in parliamentary debate settings. through this work, we hope to complement digital humanities, media, social scientists, political linguistics and any members of the public who wish to scrutinize the activities of their elected representatives. gpols makes a step towards facilitating the understanding of mp's opinions through for analyzing latent correlations and sentiments in voluminous parliamentary debates.