Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper
Query: Literature survey of sarcasm detection
Modelling Sarcasm in Twitter, a Novel Approach
Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features
UO_UPV: Deep Linguistic Humor Detection in Spanish Social Media
Irony and Sarcasm: Corpus Generation and Analysis Using Crowdsourcing
Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
On the difficulty of automatically detecting irony: beyond a simple case of negation
SarcasmBot : An open-source sarcasm-generation module for chatbots
Open Secrets and Wrong Rights: Automatic Satire Detection in English Text
Linguistic-based Patterns for Figurative Language Processing: The Case of Humor Recognition and Irony Detection
Making Computers Laugh: Investigations In Automatic Humor Recognition
Laughbot: Detecting Humor in Spoken Language with Language and Audio Cues
LEARNING TO LAUGH (AUTOMATICALLY): COMPUTATIONAL MODELS FOR HUMOR RECOGNITION
The sound of sarcasm
Applying Basic Features from Sentiment Analysis for Automatic Irony Detection
Exploring linguistic features for web spam detection: a preliminary study
Hate Speech Detection Using Natural Language Processing Techniques
Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest
Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network
a multidimensional approach for detecting irony in twitter .
Detecting Deceptive Opinion Spam using Linguistics, Behavioral and Statistical Modeling
Experimental and clinical results of breast cancer detection using UWB microwave radar
Offline bilingual word vectors, orthogonal transformations and the inverted softmax
TwitIE: An Open-Source Information Extraction Pipeline for Microblog Text
Identify and Classify Critical Success Factor of Agile Software Development Methodology Using Mind Map
Deep Text Classification Can be Fooled