Seeker Optimization with Deep Learning Enabled Sentiment Analysis on Social Media;

作     者：Hanan M.Alghamdi Saadia H.A.Hamza Aisha M.Mashraqi Sayed Abdel-Khalek 

作者机构：Department of Computer ScienceCollege of Computing Al-QunfidhahUmm Al-Qura UniversitySaudi Arabia Department of Computer ScienceCollege of Science and HumanitiesSlayelPrince Sattam Bin AbdulAziz UniversitySaudi Arabia Department of Computer ScienceCollege of Computer Science and Information SystemsNajran UnivesityNajran61441Saudi Arabia Department of MathematicsCollege of ScienceTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of MathematicsFaculty of ScienceSohag UniversitySohagEgypt 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第12期

页      面：5985-5999页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code:(22UQU4350139DSR01) 

主　　题：Sentiment analysis classification of sentiment social media seeker optimization algorithm glove embedding natural language processing 

摘      要：World Wide Web enables its users to connect among themselves through social networks,forums,review sites,and blogs and these interactions produce huge volumes of data in various forms such as emotions,sentiments,views,*** Analysis(SA)is a text organization approach that is applied to categorize the sentiments under distinct classes such as positive,negative,and ***,Sentiment Analysis is challenging to perform due to inadequate volume of labeled data in the domain of Natural Language Processing(NLP).Social networks produce interconnected and huge data which brings complexity in terms of expanding SA to an extensive array of ***,there is a need exists to develop a proper technique for both identification and classification of sentiments in social *** get rid of these problems,Deep Learning methods and sentiment analysis are consolidated since the former is highly efficient owing to its automatic learning *** current study introduces a Seeker Optimization Algorithm with Deep Learning enabled SA and Classification(SOADL-SAC)for social *** presented SOADL-SAC model involves the proper identification and classification of sentiments in social *** order to attain this,SOADL-SAC model carries out data preprocessing to clean the input *** addition,Glove technique is applied to generate the feature ***,Self-Head Multi-Attention based Gated Recurrent Unit(SHMA-GRU)model is exploited to recognize and classify the ***,Seeker Optimization Algorithm(SOA)is applied to fine-tune the hyperparameters involved in SHMA-GRU model which in turn enhances the classifier *** order to validate the enhanced outcomes of the proposed SOADL-SAC model,various experiments were conducted on benchmark *** experimental results inferred the better performance of SOADLSAC model over recent state-of-the-art approaches.