DeBERTa-GRU: Sentiment Analysis for Large Language Model

作     者：Adel Assiri Abdu Gumaei Faisal Mehmood Touqeer Abbas Sami Ullah 

作者机构：Department of Informatics for BusinessCollege of BusinessKing Khalid UniversityAbha61421Saudi Arabia Department of Computer ScienceCollege of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia School of Electrical and Information EngineeringZhengzhou UniversityZhengzhou450001China Department of Computer Science and TechnologyBeijing University of Chemical TechnologyBeijing100029China Department of Computer ScienceGovernment College University FaisalabadFaisalabadPunjab38000Pakistan 

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

年 卷 期：2024年第79卷第6期

页      面：4219-4236页

核心收录：

学科分类：1202[管理学-工商管理] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 081202[工学-计算机软件与理论] 

基　　金：King Khalid University  KKU 

主　　题：DeBERTa GRU Naive Bayes LSTM sentiment analysis large language model 

摘      要：Modern technological advancements have made social media an essential component of daily *** media allow individuals to share thoughts,emotions,and *** analysis plays the function of evaluating whether the sentiment of the text is positive,negative,neutral,or any other personal emotion to understand the sentiment context of the *** analysis is essential in business and society because it impacts strategic *** analysis involves challenges due to lexical variation,an unlabeled dataset,and text distance *** execution time increases due to the sequential processing of the sequence ***,the calculation times for the Transformer models are reduced because of the parallel *** study uses a hybrid deep learning strategy to combine the strengths of the Transformer and Sequence models while ignoring their *** particular,the proposed model integrates the Decoding-enhanced with Bidirectional Encoder Representations from Transformers(BERT)attention(DeBERTa)and the Gated Recurrent Unit(GRU)for sentiment *** the Decoding-enhanced BERT technique,the words are mapped into a compact,semantic word embedding space,and the Gated Recurrent Unit model can capture the distance contextual semantics *** proposed hybrid model achieves F1-scores of 97%on the Twitter Large Language Model(LLM)dataset,which is much higher than the performance of new techniques.