Lexical, Pragmatic and Linguistic Feature Based Two-Level Sarcasm Detection Using Machine Learning TechniquesSarcasm refers to the use of words that mean different from what a person really wants to say, especi
The three common approaches used for sarcasm detection using machine learning techniques are lexical, hyperbole and pragmatic (Ren et al., 2020, Mukhtar et al., 2018, Haripriya et al., 2017). The lexical approach uses word dictionaries to analyse the words or phrases (Vijayalaksmi and Senthil...
First, we evaluated the performance of the target detection (i.e., the classifier), the results of which are presented in Section 5.1. Then we measure the performance of extracting the target of sarcasm (i.e., the deep learning), presented in Section 5.2. To determine the efficiency of ...
“The goal ofmy present workis sarcasm detection,” Silvio Amir at the University of Lisbon, Portugal, told Digital Trends. “Given a social media post, the goal is to figure out whether a certain tweet is sarcastic or not. This is important because we’ve been using social media analysis...
Detecting Sarcasm on Twitter using both traditonal machine learning and deep learning techniques. visualizationdeep-neural-networkstwitterdeep-learningtweetssentiment-analysistext-classificationtensorflowsvmkerastopic-modelingattention-mechanismlstm-neural-networkssarcasmironysarcasm-detection ...
Evaluate the model built using f1 score Methodology: Sarcasm detection relies on the assumption that a negative situation often appears after the positive situations in a sarcastic document. (document here refers to text or tweet) [positive verb phrase] + [negative verb phrase] The dataset consis...
Sarcasm detection is a challenging task in sentiment analysis and is usually used to detect sarcasm by judging inconsistencies in the individual words of an expression of sentiment; however, detection is less effective for sentences with complex semantic information, especially those with too few sentim...
Then, they used a BiLSTM deep learning network for irony recognition, where their approach obtained an F1_score of 46%. In [6], the authors presented a method to enhance Arabic sarcasm detection. Their work is based on using the Random Forests model with data augmentation and contextual ...
sarcasm detection models ought to encode such speaker information. Current methods have achieved this by way of laborious feature engineering. By contrast, we propose to automatically learn and then exploit user embeddings, to be used in concert with lexical signals to recognize sarcasm. Our approach...
Traditional sarcasm detection algorithms often rely on a single parameter to produce their results, which is the main reason they often fall short. Gao, Nayak, and Coler instead used two complementary approaches—sentiment analysis using text and emotion recognition using audio—for a more complete ...