Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets

作     者：Aisha M.Mashraqi Hanan T.Halawani 

作者机构：College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia 

出 版 物：《Computer Systems Science & Engineering》 (计算机系统科学与工程（英文）)

年 卷 期：2023年第46卷第8期

页      面：2555-2570页

核心收录：

学科分类：0502[文学-外国语言文学] 050201[文学-英语语言文学] 05[文学] 

基　　金：The authors are thankful to the Deanship of Scientific Research at Najran University for funding this work under the National Research Priorities funding program support under code number:NU/NRP/SERC/11/3 

主　　题：Natural language processing sentiment analysis arabic tweets deep learning metaheuristics lexicon approach 

摘      要：Sentiment Analysis(SA)is one of the Machine Learning(ML)techniques that has been investigated by several researchers in recent years,especially due to the evolution of novel data collection methods focused on social *** literature,it has been reported that SA data is created for English language in excess of any other *** is challenging to perform SA for Arabic Twitter data owing to informal nature and rich morphology of Arabic *** earlier study conducted upon SA for Arabic Twitter focused mostly on automatic extraction of the features from the *** word embedding has been employed in literature,since it is less labor-intensive than automatic feature *** ignoring the context of sentiment,most of the word-embedding models follow syntactic data of *** current study presents a new Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets(DFODLSAAT)*** aim of the presented DFODL-SAAT model is to distinguish the sentiments from opinions that are tweeted in Arabic *** first,data cleaning and pre-processing steps are performed to convert the input tweets into a useful *** addition,TF-IDF model is exploited as a feature extractor to generate the feature ***,Attention-based Bidirectional Long Short Term Memory(ABLSTM)technique is applied for identification and classification of *** last,the hyperparameters of ABLSTM model are optimized using DFO *** performance of the proposed DFODL-SAAT model was validated using the benchmark dataset and the outcomes were investigated under different *** experimental outcomes highlight the superiority of DFODL-SAAT model over recent approaches.