Longgang Zhao1, Seok-Won Lee2,*
CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1855-1877, 2024, DOI:10.32604/cmc.2024.056215
- 15 October 2024
Abstract 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 domain-specific corpora by leveraging techniques such as word-frequency analysis and word embedding. More >
Login
Register
