Paper Reading AI Learner

A Stylistic Analysis of Honest Deception: The Case of Seinfeld TV Series Sitcom

2021-04-17 17:17:03
Mohcine El Baroudi

Abstract

Language is a powerful tool if used in the correct manner. It is the major mode of communication, and using the correct choice of words and styles can serve to have a long-lasting impact. Stylistics is the study of the use of various language styles in communication to pass a message with a bigger impact or to communicate indirectly. Stylistic analysis, therefore, is the study of the use of linguistic styles in texts to determine how a style has been used, what is communicated and how it is communicated. Honest deception is the use of a choice of words to imply something different from the literal meaning. A person listening or reading a text where honest deception has been used and with a literal understanding may completely miss out on the point. This is because the issue of honesty and falsehood arises. However, it would be better to understand that honest deception is used with the intention of having a lasting impact rather than to deceive the readers, viewers or listeners. The major styles used in honest deception are hyperboles, litotes, irony and sarcasm. The Seinfeld Sitcom TV series was a situational TV comedy show aired from 1990 to 1998. the show attempts to bring to the understanding the daily life of a comedian and how comedian views life experiences and convert them into hilarious jokes. It also shows Jerry's struggle with getting the right partner from the many women who come into his life. Reflecting on honest deception in the Seinfeld sitcom TV series, this paper is going to investigate how honest deception has been used in the series, why it has been used and what is being communicated. The study is going to use a recapitulative form to give a better analysis and grouping of the different styles used in honest deception throughout the series.

Abstract (translated)

URL

https://arxiv.org/abs/2104.08599

PDF

https://arxiv.org/pdf/2104.08599.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot