Open Access

ARTICLE

Sentiment Analysis and Classification Using Deep Semantic Information and Contextual Knowledge

Ahmed Abdulhakim Al-Absi¹, Dae-Ki Kang^2,*, Mohammed Abdulhakim Al-Absi³

1 Department of Smart Computing, Kyungdong University 46 4-gil, Bongpo, Gosung, Gangwon-do, 24764, Korea
2 Division of Computer Engineering, Dongseo University, 47 Jurye-ro, Sasang-gu, Busan, 47011, Korea
3 Department of Computer Engineering, Dongseo University, 47 Jurye-ro, Sasang-gu, Busan, 47011, Korea

* Corresponding Author: Dae-Ki Kang. Email:

Computers, Materials & Continua 2023, 74(1), 671-691. https://doi.org/10.32604/cmc.2023.030262

Received 22 March 2022; Accepted 09 June 2022; Issue published 22 September 2022

Abstract

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 baseline methods in terms of accuracy and Macro-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.

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Keywords

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