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

作　　者：Abdul Rahaman Wahab Sait Mohamad Khairi Ishak 

机构地区：[1]Department of Documents and Archive,Center of Documents and Administrative Communication,King Faisal University,Al Hofuf,Al-Ahsa,31982,Saudi Arabia [2]School of Electrical and Electronic Engineering,Engineering Campus,Universiti Sains Malaysia(USM),Nibong Tebal,Penang,14300,Malaysia

出　　处：《Computer Systems Science & Engineering》2023年第3期2553-2567,共15页计算机系统科学与工程（英文）

基　　金：supported through the Annual Funding track by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia[Project No.AN000685].

摘　　要：Sentiment analysis(SA)is the procedure of recognizing the emotions related to the data that exist in social networking.The existence of sarcasm in tex-tual 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.

关 键 词：Sentiment analysis sarcasm detection deep learning natural language processing N-GRAMS hyperparameter tuning 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术]