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Record W2410695560 · doi:10.5539/ells.v6n2p193

A Pragmatic Analysis of Humor Words in English Advertisements

2016· article· en· W2410695560 on OpenAlexvenueno aff
Xiaqing Li

Bibliographic record

VenueEnglish Language and Literature Studies · 2016
Typearticle
Languageen
FieldPsychology
TopicLanguage, Metaphor, and Cognition
Canadian institutionsnot available
Fundersnot available
KeywordsCooperative principleLinguisticsNewspaperGrammarPresuppositionDeixisSituational ethicsPragmaticsVocabularyPerspective (graphical)RhetoricHumor researchPsychologySpeech actEuphemismTarget audienceFocus (optics)PolitenessAdvertisingComputer scienceGriceArtificial intelligence

Abstract

fetched live from OpenAlex

<p>As an independent discipline, pragmatics was through out thirty years’ development. It is also a young discipline. As a medium emerging commonly in advertising language art, humor has attracted wide attention of many producers. Previous scholars analyzed more from the perspective of grammar, vocabulary, rhetoric, etc. But the research of advertising language humor is lacking from the aspects of pragmatic rules. Our collection of data is quite open. Any advertisement that can be transcribed in to written form is our interest. They are excerpted from magazines, advertisement, collecting books, newspapers, TV and radio commercials. As we only focus on language humor, situational humor produced by visual performance is not involved this thesis. The advertising language art of humor has been widely paid attention. Based on the existing theories of humor research, author of this paper used many kinds of pragmatic theories to analyze English advertising humor language, and including reference, deixis, anaphora, presupposition, speech act theory, the cooperative principle, conversational implicatures, and the politeness principle. It can not only provide reference for the research of this field for later scholars, but also provide theoretical guidance for the AD makers of using humor language to produce a good advertising effect.</p>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.061
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.292
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2016
Admission routes1
Has abstractyes

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