Sentiment analysis(AS)is one of the basic research directions in natural language processing(NLP),it is widely adopted for news,product review,and politics.Aspect-based sentiment analysis(ABSA)aims at identifying the ...Sentiment analysis(AS)is one of the basic research directions in natural language processing(NLP),it is widely adopted for news,product review,and politics.Aspect-based sentiment analysis(ABSA)aims at identifying the sentiment polarity of a given target context,previous existing model of sentiment analysis possesses the issue of the insufficient exaction of features which results in low accuracy.Hence this research work develops a deep-semantic and contextual knowledge networks(DSCNet).DSCNet tends to exploit the semantic and contextual knowledge to understand the context and enhance the accuracy based on given aspects.At first temporal relationships are established then deep semantic knowledge and contextual knowledge are introduced.Further,a deep integration layer is introduced to measure the importance of features for efficient extraction of different dimensions.Novelty of DSCNet model lies in introducing the deep contextual.DSCNet is evaluated on three datasets i.e.,Restaurant,Laptop,and Twitter dataset considering different deep learning(DL)metrics like precision,recall,accuracy,and Macro-F1 score.Also,comparative analysis is carried out with different baselinemethods in terms of accuracy andMacro-F1 score.DSCNet achieves 92.59%of accuracy on restaurant dataset,86.99%of accuracy on laptop dataset and 78.76%of accuracy on Twitter dataset.展开更多
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(NRF-2022R1A2C2012243).
文摘Sentiment analysis(AS)is one of the basic research directions in natural language processing(NLP),it is widely adopted for news,product review,and politics.Aspect-based sentiment analysis(ABSA)aims at identifying the sentiment polarity of a given target context,previous existing model of sentiment analysis possesses the issue of the insufficient exaction of features which results in low accuracy.Hence this research work develops a deep-semantic and contextual knowledge networks(DSCNet).DSCNet tends to exploit the semantic and contextual knowledge to understand the context and enhance the accuracy based on given aspects.At first temporal relationships are established then deep semantic knowledge and contextual knowledge are introduced.Further,a deep integration layer is introduced to measure the importance of features for efficient extraction of different dimensions.Novelty of DSCNet model lies in introducing the deep contextual.DSCNet is evaluated on three datasets i.e.,Restaurant,Laptop,and Twitter dataset considering different deep learning(DL)metrics like precision,recall,accuracy,and Macro-F1 score.Also,comparative analysis is carried out with different baselinemethods in terms of accuracy andMacro-F1 score.DSCNet achieves 92.59%of accuracy on restaurant dataset,86.99%of accuracy on laptop dataset and 78.76%of accuracy on Twitter dataset.
