An Intelligent Metaheuristic Optimization with Deep Convolutional Recurrent Neural Network Enabled Sarcasm Detection and Classification Model

Abstract

Sarcasm is a state of speech in which the speaker says something that is externally unfriendly with a purpose of abusing/deriding the listener and/or a third person. Since sarcasm detection is mainly based on the context of utterances or sentences, it is hard to design a model to proficiently detect sarcasm in the domain of natural language processing (NLP). Despite the fact that various methods for detecting sarcasm have been created utilizing statistical machine learning and rule-based approaches, they are unable of discerning figurative meanings of words. The models developed using deep learning approaches have shown superior performance for sarcasm detection over traditional approaches. With this motivation, this paper develops novel deep learning (DL) enabled sarcasm detection and classification (DLE-SDC) model. The DLE-SDC technique primarily involves pre-processing stage which encompasses single character removal, multispaces removal, URL removal, stop word removal, and tokenization. Next to data preprocessing, the preprocessed data is converted into the feature vector by Glove Embeddings technique. Followed by, convolutional neural network with recurrent neural network (CNN-RNN) technique is utilized to detect and classify sarcasm. In order to boost the detection outcomes of the CNN+RNN technique, a hyper parameter tuning process utilizing teaching and learning based optimization (TLBO) algorithm is employed in such a way that the classification performance gets increased. The DLE-SDC model is validated using the benchmark dataset and the performance is examined interms of precision, recall, accuracy, and F1-score.

A Survey of Sentiment Analysis and Sarcasm Detection

“…The proposed model employs pre-processing, pre-training, and classification phases, utilizing three sentence-based techniques to provide the final categorization of sarcastic or non-sarcastic comments. Kavitha et al [19] developed a novel deep learningbased sarcasm detection classification model (DLE-SDC) that involves a pre-processing stage, Glove-based word vector representation, a combination of convolutional neural network and recurrent neural network (CNN-RNN) based classification, and an algorithm for teaching and learning based optimization (TLBO) for hyperparameter tuning. The model is validated using a benchmark dataset and evaluated based on precision, recall, accuracy, and F1 score.…”

“…Sarcasm [14][15][16][17][18], a statement conveying the as it can invert the true sentiment of a statement. Despite being a popular research topic, sarcasm detection [19][20][21][22][23][24][25][26] is crucial as sentiment analysis can misinterpret sarcastic sentences, leading to inaccurate sentiment classifications. The difficulty lies in the nuanced nature of human emotions and expressions conveyed through text, making automatic sarcasm detection a challenging task within natural language processing (NLP) [27,28].…”

Primal-Optimal-Binding LPNet: Deep Learning Architecture to Predict Optimal Binding Constraints of a Linear Programming Problem

“…CNN with recurrent neural network (CNN-RNN) technique is utilized to detect and classify sarcasm [28]. In order to boost the detection outcomes of the CNN+RNN technique, a hyperparameter tuning process utilizing a teaching and learning-based optimization (TLBO) algorithm is employed in such a way that the classification performance gets increased.…”