Deep Learning with Natural Language Processing Enabled Sentimental Analysis on Sarcasm Classification

Abstract

Sentiment analysis (SA) is the procedure of recognizing the emotions related to the data that exist in social networking. The existence of sarcasm in textual data is a major challenge in the efficiency of the SA. Earlier works on sarcasm detection on text utilize lexical as well as pragmatic cues namely interjection, punctuations, and sentiment shift that are vital indicators of sarcasm. With the advent of deep-learning, recent works, leveraging neural networks in learning lexical and contextual features, removing the need for handcrafted feature. In this aspect, this study designs a deep learning with natural language processing enabled SA (DLNLP-SA) technique for sarcasm classification. The proposed DLNLP-SA technique aims to detect and classify the occurrence of sarcasm in the input data. Besides, the DLNLP-SA technique holds various sub-processes namely preprocessing, feature vector conversion, and classification. Initially, the pre-processing is performed in diverse ways such as single character removal, multi-spaces removal, URL removal, stopword removal, and tokenization. Secondly, the transformation of feature vectors takes place using the N-gram feature vector technique. Finally, mayfly optimization (MFO) with multi-head self-attention based gated recurrent unit (MHSA-GRU) model is employed for the detection and classification of sarcasm. To verify the enhanced outcomes of the DLNLP-SA model, a comprehensive experimental investigation is performed on the News Headlines Dataset from Kaggle Repository and the results signified the supremacy over the existing approaches.

A Survey of Sentiment Analysis and Sarcasm Detection

“…In the sentiment analysis phase, various classifiers undergo training using TF-IDF vectorization and n-gram features, with the best results obtained using the trigram feature and linear SVM. Sait et al [39] propose a novel approach called deep learning with natural language processing enabled SA (DLNLP-SA) for detecting sarcasm in textual data and its impact on sentiment analysis. The paper introduces the DLNLP-SA technique, which involves pre-processing, feature vector conversion using the N-gram feature extraction technique, and sarcasm classification using the MHSA-GRU (multi-head self-attention-based gated recurrent unit) model.…”

Applied Linguistics With Red-Tailed Hawk Optimizer-Based Ensemble Learning Strategy in Natural Language Processing

“…Sait and Ishak [11] present a DL with an NLP-enabled SA method for the classification of sarcasm. The preprocessing is implemented at first in various forms viz., multiple space removal, single character elimination, stopword removal, tokenization, and URL removal.…”

“…( 10), 𝑥 and 𝑦 indicate direction coordinates that are evaluated according to Eq. ( 11): Where 𝐴 means the angel gain [5][6][7][8][9][10][11][12][13][14][15], 𝑟 is a control gain [1,2], 𝑅 0 shows the primary value within [0.5−3], and 𝑟𝑎𝑛𝑑 denotes the randomly generated number [0,1]. This parameter helps the hawk to fly around the target with spiral movement as follows:…”

Design of English Grammar Error Correction System Based on Deep Learning Algorithms

“…They believe that DL algorithms have become the mainstream algorithms in the field of natural language processing, and they have a wide range of applications in text classification, sentiment analysis, machine translation, question-answering systems, etc. [5][6]. This paper will explore the design of an EG correction system based on DL algorithms, mainly to improve the grammar level and communication effectiveness of English learners.…”