Multi-aspect Multilingual and Cross-lingual Parliamentary Speech Analysis

Miok, Kristian; Tenorio, Encarnación Hidalgo; Osenova, Petya; Benítez-Castro, Miguel-Ángel; Robnik–Šikonja, Marko

doi:10.48550/arxiv.2207.01054

Cited by 2 publications

(2 citation statements)

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“…Numerous political scientists have observed that parliamentary texts contain valuable information, such as the politician's attitudes, positions, political objectives, and policy inclinations (Miok et al 2022; Proksch and Slapin 2010); however, manually identifying such information from vast amounts of text is extremely challenging. In NLP, topic modeling is a mature and widely used method, which has demonstrated its effectiveness in extracting important information from political speech.…”

Section: Quantifying Assembly Activities Using Topic Modelingmentioning

confidence: 99%

Quantifying the Activities of Local Assembly Members in Japan: Recent Advances and an Approach Using the BERTopic Model

LI,

KAWAMURA,

KIMURA

2024

IIS

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In Japan, the activities of local assembly members in prefectural assembly sessions are crucial within the framework of representative democracy. This study provides a comprehensive review of existing research that quantifies the activities of local assembly members through topic modeling. Furthermore, the BERTopic model was implemented to perform semantic clustering and topic labeling by combining bidirectional encoder representations from transformer (BERT) embeddings and clustering algorithms. The researchers used a dataset of local assembly minutes transformed into a machine-readable format and selected a subset of general questions (159 general questions from 32 members between 2011 and 2015, and 122 general questions from 23 members between 2016 and 2020) in Fukushima Prefecture. The results demonstrate that BERTopic exhibits robust semantic representation and visualization capabilities, thereby improving the understanding of relationships among the main topics. Compared to the Latent Dirichlet Allocation (LDA) and the Structural Topic Model (STM), BERTopic outperformed in terms of identifying subtle topics and yielded a higher topic coherence score, indicating more meaningful and consistent thematic extraction. Furthermore, this research contributes to documenting the recovery process in disaster-affected prefectures after the Great East Japan Earthquake and provides reference material for recovery efforts.

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Section: Quantifying Assembly Activities Using Topic Modelingmentioning

confidence: 99%

Quantifying the Activities of Local Assembly Members in Japan: Recent Advances and an Approach Using the BERTopic Model

LI,

KAWAMURA,

KIMURA

2024

IIS

View full text Add to dashboard Cite

show abstract

“…Gender is one of the most commonly available metadata in public speech datasets. Gender itself is also the focus of several speech technology tasks such as estimating the binary gender of a speaker [1][2][3], and anonymising a speaker through gender deidentification [4][5][6]. State-of-the-art systems have achieved scores very close to 100% recognition rate [1].…”

Section: Introductionmentioning

confidence: 99%