Integrating Ontology-Based Approaches with Deep Learning Models for Fine-Grained Sentiment Analysis  

作　　者：Longgang Zhao Seok-Won Lee 

机构地区：[1]The Knowledge-Intensive Software Engineering(NiSE)Research Group,Department of Artificial Intelligence,Ajou University,Suwon City,16499,Republic of Korea [2]Department of Software and Computer Engineering,Ajou University,Suwon City,16499,Republic of Korea

出　　处：《Computers, Materials & Continua》2024年第10期1855-1877,共23页计算机、材料和连续体（英文）

基　　金：supported by the BK21 FOUR Program of the National Research Foundation of Korea funded by the Ministry of Education(NRF5199991014091);Seok-Won Lee’s work was supported by Institute of Information&Communications Technology Planning&Evaluation(IITP)under the Artificial Intelligence Convergence Innovation Human Resources Development(IITP-2024-RS-2023-00255968)grant funded by the Korea government(MSIT).

摘　　要：Although sentiment analysis is pivotal to understanding user preferences,existing models face significant challenges in handling context-dependent sentiments,sarcasm,and nuanced emotions.This study addresses these challenges by integrating ontology-based methods with deep learning models,thereby enhancing sentiment analysis accuracy in complex domains such as film reviews and restaurant feedback.The framework comprises explicit topic recognition,followed by implicit topic identification to mitigate topic interference in subsequent sentiment analysis.In the context of sentiment analysis,we develop an expanded sentiment lexicon based on domainspecific corpora by leveraging techniques such as word-frequency analysis and word embedding.Furthermore,we introduce a sentiment recognition method based on both ontology-derived sentiment features and sentiment lexicons.We evaluate the performance of our system using a dataset of 10,500 restaurant reviews,focusing on sentiment classification accuracy.The incorporation of specialized lexicons and ontology structures enables the framework to discern subtle sentiment variations and context-specific expressions,thereby improving the overall sentiment-analysis performance.Experimental results demonstrate that the integration of ontology-based methods and deep learning models significantly improves sentiment analysis accuracy.

关 键 词：Deep learning ONTOLOGY fine-grained sentiment analysis online reviews 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]