www.athenarc.gr

Exploring the Political Agenda of the Greek

Parliament Plenary SessionsDimitris Gkoumas, Maria Pontiki, Konstantina Papanikolaou, and Haris Papageorgiou

ATHENA Research & Innovation Centre/Institute for Language & Speech Processing(ATHENA RIC/ILSP), Greece.

ParlaCLARIN@LREC2018, 7 May 2018, Miyazaki, Japan

“APOLLONIS, Greek Infrastructure for Digital Arts and Humanities, Language Research and Innovation” (MIS 5002738)

“Computational Science and Technologies: Data, Content and Interaction” (MIS 5002437)

www.athenarc.gr

•Capturing the way political and policyagendas are shaped and evolve overtime.

•Reflecting the stance and the politicalbehavior under which individualParliament Members and politicalgroups react and act over time.

Parliament plenary sessions: Valuable Data Sources

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Insights about:•the way in which the European Parliament reacts to exogenous events like the Eurocrisis (Greene and Gross,2017)

•the political attention of individual U.S. Senators (Quinn et al., 2010)

Parliament plenary sessions & Topic Modelling

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Image source: https://theintelligenceofinformation.wordpress.com/2016/12/06/topic-modeling-latent-dirichlet-allocation-vs-correlation-explanation-alternative/

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Exploring the Political Agenda of the Greek Parliament Plenary Sessions

Methodology: Overview

Data Collection Data Preparation Preprocessing Organization Topic Modelling Visualization

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Data Collection

Data Collection

Data Preparation Preprocessing Organization Topic

Modelling Visualization

1989 to 2015 proceedingsà4356 plenary sessions

à1.063.546 unique speeches

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Data Preparation

Different formats (txt/doc/docx/pdf/jpeg)

à structured and machine-readable xmlversion

Data Collection

Data Preparation

Preprocessing Organization Topic Modelling Visualization

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Preprocessing

Data Collection

Data Preparation

Preprocessing

Organization Topic Modelling Visualization

ü Segment annotation: the start/end point of each speech

ü Temporal annotation: the date of each speech

ü Speaker annotation: the name of each speaker and his/her politicalaffiliation

ü POS tagging7/20

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Data Organization

ü time windows of fixed durationà 1 month

ü 283 windows for the time period from 1989 to 2015

àdiscover topics within the monthly proceedings (e.g. segments)& monitor their dynamics over time

Data Collection

Data Preparation Preprocessing

Organization

Topic Modelling Visualization

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Topic Modelling

Data Collection

Data Preparation Preprocessing Organization

Topic Modelling

Visualization

q Dynamic Non-negative Matrix Factorization (NMF) approach (Greene and Cross, 2017).

q After identifying a certain number of topics in each 1-month-window, the ones which are semantically similar are groupedtogether as “dynamic topics”, rather than identifying topics from the beginning.

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Dynamic NMF

Input • Processed and organized data• 283 monthly windows,

1.063.546 unique speeches

Output • 283 window-based topics

• 90 dynamic topics

• Document term-matrix TF-IDF normalized• -stop words, +content words• -rare terms & units with length less than three

lines, +nouns & adjectives

• NMF model generates topics for each windowPhase 1

• NMF algorithm is applied again, with the number of k topics decided after applying the Topic Coherence via Word2Vec (TC-W2V)

• GrParl speeches used as a training corpus for the extraction of the word embeddings

Phase 2

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Visualization

Data Collection Data Preparation Preprocessing Organization Topic Modelling

Visualization

ü By topic

ü By time

ü Search function

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Greek Parliament Plenary Sessions presented by topic

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20 most prominent words for the dynamic topic D79

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Involved Parliament Members

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Contribution of each political party to a dynamic topic

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Contribution of each region to a dynamic topic

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Most important window-topics related to a dynamic topic

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Greek Parliament Plenary Sessions presented by time

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DiscussionC

ontri

butio

nTransformation of the Greek Parliament plenary sessions minutes into a structured and machine readable corpus

√ For the first time available forNatural Language Processing andText Mining approaches

Insightful navigation across the Greek Parliament corpus

√ Topic based presentation√ Time-window based presentation √ Search function

Futu

re W

ork Qualitative analysis of the extracted dynamic topics

& assignment of descriptive labels

Exploration of the evolution of selected topics (e.g. refugee crisis, Eurocrisis) & comparison with the European Parliament and/or other European countries

Enrichment with more insights e.g. Quotations, Sentiment

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Dimitris Gkoumas, Maria Pontiki, Konstantina Papanikolaou, and Haris PapageorgiouATHENA-Research & Innovation Centre in Information, Communication and Knowledge Technologies (ATHENA RIC), Greece.

Exploring the Political Agenda of the Greek Parliament Plenary Sessions

ParlaCLARIN@LREC2018, 7 May 2018, Miyazaki, Japan

“Computational Science and Technologies: Data, Content and Interaction” (MIS 5002437)“APOLLONIS, Greek Infrastructure for Digital Arts and Humanities, Language Research

and Innovation” (MIS 5002738)