Stylistic Analysis of the French Presidential Speeches: Is Macron really
different?
- URL: http://arxiv.org/abs/2105.02844v1
- Date: Thu, 6 May 2021 17:35:31 GMT
- Title: Stylistic Analysis of the French Presidential Speeches: Is Macron really
different?
- Authors: Dominique Labb\'e, Jacques Savoy
- Abstract summary: This study shows that de Gaulle's rhetoric is not mainly dedicated to his own person, or that the two terms of J. Chirac are not fully similar.
According to several overall stylistic indicators, Macron's style does not appear as complex compared to his predecessors.
Compared to the recent US presidents, the French ones present some similarities (e.g., similar mean sentence length) and dissimilarities (more I-words, less we-words)
- Score: 4.5687771576879594
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Presidential speeches indicate the government's intentions and justifications
supported by a dedicated style and rhetoric oscillating between explanation and
controversy. Over a period of sixty years, can we observe stylistic variations
by the different French presidents of the Fifth Republic (1958-2018)? Based on
official transcripts of all their allocution, this paper illustrates the
stylistic evolution and presents the underlying main trends. This study shows
that de Gaulle's rhetoric is not mainly dedicated to his own person, or that
the two terms of J. Chirac are not fully similar. According to several overall
stylistic indicators, Macron's style does not appear as complex compared to his
predecessors (F. Hollande or N. Sarkozy) but a more careful analysis clearly
demonstrates his noticeable new style. Compared to the recent US presidents,
the French ones present some similarities (e.g., similar mean sentence length)
and dissimilarities (more I-words, less we-words). In this comparative
analysis, Macron's style is also clearly distinctive from both the US and
former French presidents. Opting for a more abstract discourse, less anchored
in space, using less numbers, E. Macron tends to use long sentences. These
various stylistic and rhetorical features could explain his being misunderstood
by the French people and his recurrent low approval ratings.
Related papers
- Language Proficiency and F0 Entrainment: A Study of L2 English Imitation in Italian, French, and Slovak Speakers [48.3822861675732]
This study explores F0 entrainment in second language (L2) English speech imitation during an Alternating Reading Task (ART)
participants with Italian, French, and Slovak native languages imitated English utterances.
Results indicate a nuanced relationship between L2 English proficiency and entrainment.
arXiv Detail & Related papers (2024-04-16T10:10:19Z) - Quantifying the Uniqueness of Donald Trump in Presidential Discourse [51.76056700705539]
This paper introduces a novel metric of uniqueness based on large language models.
We find considerable evidence that Trump's speech patterns diverge from those of all major party nominees for the presidency in recent history.
arXiv Detail & Related papers (2024-01-02T19:00:17Z) - Narrowing the Gap between Zero- and Few-shot Machine Translation by
Matching Styles [53.92189950211852]
Large language models have demonstrated their ability to generalize to machine translation using zero- and few-shot examples with in-context learning.
In this paper, we investigate the factors contributing to this gap and find that this gap can largely be closed (for about 70%) by matching the writing styles of the target corpus.
arXiv Detail & Related papers (2023-11-04T03:18:45Z) - That was the last straw, we need more: Are Translation Systems Sensitive
to Disambiguating Context? [64.38544995251642]
We study semantic ambiguities that exist in the source (English in this work) itself.
We focus on idioms that are open to both literal and figurative interpretations.
We find that current MT models consistently translate English idioms literally, even when the context suggests a figurative interpretation.
arXiv Detail & Related papers (2023-10-23T06:38:49Z) - Cross-Lingual Speaker Identification Using Distant Supervision [84.51121411280134]
We propose a speaker identification framework that addresses issues such as lack of contextual reasoning and poor cross-lingual generalization.
We show that the resulting model outperforms previous state-of-the-art methods on two English speaker identification benchmarks by up to 9% in accuracy and 5% with only distant supervision.
arXiv Detail & Related papers (2022-10-11T20:49:44Z) - United States Politicians' Tone Became More Negative with 2016 Primary
Campaigns [11.712441267029092]
We apply psycholinguistic tools to a novel, comprehensive corpus of 24 million quotes from online news attributed to 18,627 US politicians.
We show that, whereas the frequency of negative emotion words had decreased continuously during Obama's tenure, it suddenly and lastingly increased with the 2016 primary campaigns.
This work provides the first large-scale data-driven evidence of a drastic shift toward a more negative political tone following Trump's campaign start as a catalyst.
arXiv Detail & Related papers (2022-07-17T08:41:14Z) - Corpus and Models for Lemmatisation and POS-tagging of Old French [0.0]
We present the current results of a long going project providing lemmatisation andPOS models for Old French.
We describe how we broached the difficult question of providing lemmatisation andPOS models for Old French with the help of neural taggers and the progressive constitution of dedicated corpora.
arXiv Detail & Related papers (2021-09-23T15:32:41Z) - How Metaphors Impact Political Discourse: A Large-Scale Topic-Agnostic
Study Using Neural Metaphor Detection [29.55309950026882]
We present a large-scale data-driven study of metaphors used in political discourse.
We show that metaphor use correlates with ideological leanings in complex ways that depend on concurrent political events such as winning or losing elections.
We show that posts with metaphors elicit more engagement from their audience overall even after controlling for various socio-political factors such as gender and political party affiliation.
arXiv Detail & Related papers (2021-04-08T17:16:31Z) - My Teacher Thinks The World Is Flat! Interpreting Automatic Essay
Scoring Mechanism [71.34160809068996]
Recent work shows that automated scoring systems are prone to even common-sense adversarial samples.
We utilize recent advances in interpretability to find the extent to which features such as coherence, content and relevance are important for automated scoring mechanisms.
We also find that since the models are not semantically grounded with world-knowledge and common sense, adding false facts such as the world is flat'' actually increases the score instead of decreasing it.
arXiv Detail & Related papers (2020-12-27T06:19:20Z) - Is Japanese gendered language used on Twitter ? A large scale study [0.0]
This study starts from a collection of 408 million Japanese tweets from 2015 till 2019 and an additional sample of 2355 manually classified Twitter accounts timelines into gender and categories (politicians, musicians, etc)
A large scale textual analysis is performed on this corpus to identify and examine sentence-final particles (SFPs) and first-person pronouns appearing in the texts.
It turns out that gendered language is in fact used also on Twitter, in about 6% of the tweets, and that the prescriptive classification into "male" and "female" language does not always meet the expectations, with remarkable exceptions.
arXiv Detail & Related papers (2020-06-29T11:07:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.