Target-Specific Convolutional Bi-directional LSTM Neural Network for Political Ideology Analysis

Li, Xilian; Chen, Wei; Wang, Tengjiao; Huang, Weijing

doi:10.1007/978-3-319-63564-4_5

Cited by 5 publications

(7 citation statements)

References 17 publications

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“…Others have utilised similar techniques to facilitate other tasks. For example, Duthie et al (2016) attempt to identify the 'ethos' of speakers in the UK Parliament, while Li et al (2017) detect political ideology in those of the US Congress. Meanwhile, political scientists, such as Proksch and Slapin (2010) and Lauderdale and Herzog (2016) have analysed debates to position speakers on a range of scales related to policy and ideology.…”

Section: Related Workmentioning

confidence: 99%

Identifying Opinion-Topics and Polarity of Parliamentary Debate Motions

Abercrombie¹,

Batista-Navarro

2018

Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Analysis of the topics mentioned and opinions expressed in parliamentary debate motionsor proposals-is difficult for human readers, but necessary for understanding and automatic processing of the content of the subsequent speeches. We present a dataset of debate motions with pre-existing 'policy' labels, and investigate the utility of these labels for simultaneous topic and opinion polarity analysis. For topic detection, we apply one-versus-therest supervised topic classification, finding that good performance is achieved in predicting the policy topics, and that textual features derived from the debate titles associated with the motions are particularly indicative of motion topic. We then examine whether the output could also be used to determine the positions taken by proposers towards the different policies by investigating how well humans agree in interpreting the opinion polarities of the motions. Finding very high levels of agreement, we conclude that the policies used can be reliable labels for use in these tasks, and that successful topic detection can therefore provide opinion analysis of the motions 'for free'.

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Section: Related Workmentioning

confidence: 99%

Identifying Opinion-Topics and Polarity of Parliamentary Debate Motions

Abercrombie¹,

Batista-Navarro

2018

Proceedings of the 9th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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“…On that account, researchers consider one of two methods to label political ideologies of a textual news article, namely, the top-to-bottom method or bottom-to-top method. In the top-to-bottom approach [14][15][16][17][18][19], textual articles are labeled with the ideology corresponding to its author's partisanship or explicit political ideology. As an example of top-to-bottom approach Preoţiuc-Pietro et al [14] collected tweets from users participated in questioner to determine their political ideology.…”

Section: Related Workmentioning

confidence: 99%

“…News sources political ideology rating is derived from the ALLSIDES.COM. Others Rahat et al [16], Li et al [17] and Bayram et al [19] relied on publicly available resources of parliamentary and Congressional debates marked with party affiliations.…”

Section: Related Workmentioning

confidence: 99%

“…One can find ideologized text in social media [14], campaign speeches [21], congressional transcripts, debates [16,19,20], news websites or blogs [15,20]. While we are addressing the problem of political alignments in relatively long textual news articles, ideology detection models could be applied to sentences [20], short texts [14,17,21], and documents [15,16,[18][19][20]. We collected textual political posts tagged with politicians' names from various news websites and blogs; however, others collected politically ideologized texts within selected sets of topics [21] or unbounded by any topics [14][15][16][18][19][20].…”

Section: Related Workmentioning

confidence: 99%

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Political Ideology Detection of News Articles Using Deep Neural Networks

Alzhrani¹

2022

Intelligent Automation &Amp; Soft Computing

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Individuals inadvertently allow emotions to drive their rational thoughts to predetermined conclusions regarding political partiality issues. Being wellinformed about the subject in question mitigates emotions' influence on humans' cognitive reasoning, but it does not eliminate bias. By nature, humans tend to pick a side based on their beliefs, personal interests, and principles. Hence, journalists' political leaning is defining factor in the rise of the polarity of political news coverage. Political bias studies usually align subjects or controversial topics of the news coverage to a particular ideology. However, politicians as private citizens or public officials are also consistently in the media spotlight throughout their careers. Detecting political polarity in the news coverage of politicians rather than topics adds a new perspective. Determining the best approach for detecting political polarity in the news relies on the news delivery method. Data types such as videos, audio, or text could summarize the news delivery methods. Text is one of the most prominent news delivery methods. Text pattern recognition and text classification are well-established research areas with applications in many multidisciplinary domains. We propose to use deep neural networks to detect ideology in news media articles that cover news related to political officials, namely, President Obama and Trump. Deep network models were able to identify the political ideology of articles with over 0.9 F1-Score. An evaluation and analysis of deep neural network performance in detecting political ideology of news articles, articles' authors, and news sources are presented in the paper. Furthermore, this paper experiments on and provides a detailed analysis of newly reconstructed datasets.

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“…• Textual: including word (all studies) or character (Kapočiūtė-Dzikienė & Krupavičius, 2014) n-grams, punctuation, embeddings, custom dictionary keyword features (Budhwar et al, 2018), words from particular grammatical categories (Iyyer et al, 2014;Kapočiūtė-Dzikienė & Krupavičius, 2014;Lapponi et al, 2018;Monroe et al, 2017;Naderi & Hirst, 2016;Onyimadu et al, 2013;Sokolova & Lapalme, 2008;Van der Zwaan et al, 2016), presence of questions (Budhwar et al, 2018), word embeddings (Bhatia & P, 2018;Glavaš et al, 2017;Iyyer et al, 2014;Ji & Smith, 2017;Li et al, 2017;Naderi & Hirst, 2016;Rheault, 2016;Rheault et al, 2016) and sentence (Rudkovsky et al, 2018) embeddings, and parse trees (Balahur et al, 2009;Iyyer et al, 2014;Ji & Smith, 2017).…”

Section: Language Models and Feature Selectionmentioning

confidence: 99%

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

Abercrombie

Batista-Navarro

2020

J Comput Soc Sc

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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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Target-Specific Convolutional Bi-directional LSTM Neural Network for Political Ideology Analysis

Cited by 5 publications

References 17 publications

Identifying Opinion-Topics and Polarity of Parliamentary Debate Motions

Identifying Opinion-Topics and Polarity of Parliamentary Debate Motions

Political Ideology Detection of News Articles Using Deep Neural Networks

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

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