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Integrated Two Variant Deep Learners for Aspect-Based Sentiment Analysis: An Improved Meta-Heuristic-Based Model.

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Název: Integrated Two Variant Deep Learners for Aspect-Based Sentiment Analysis: An Improved Meta-Heuristic-Based Model.
Autoři: Datta, Samik, Chakrabarti, Satyajit
Zdroj: Cybernetics & Systems; 2024, Vol. 55 Issue 7, p1631-1667, 37p
Témata: CONVOLUTIONAL neural networks, RECURRENT neural networks, OPTIMIZATION algorithms, DEEP learning, WEB portals
Abstrakt: Several traditional methods were tested on standard datasets to evaluate client emotions transmitted via internet portals. Customers, on the other hand, continue to have difficulty obtaining aspect-oriented viewpoints voiced by other customers, and the accuracy of the current model is insufficient. The suggested Aspect-Based Sentimental Analysis (ABSA) starts with pre-processing, which includes "stop word and punctuation removal, lower case conversion, and stemming." Aspect extraction, which entails dividing the nouns and adjectives, as well as verbs and adverbs, is the following step. The weighted polarity features from the "Vader sentiment intensity analyzer, as well as the word2vector and Term Frequency-Inverse Document Frequency (TF-IDF)" are concatenated. OIDL stands for Optimized Integrated Deep Learning, which combines two types of deep learners. The first is the combination of concatenated features with "Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)," while the second is the combination of concatenated features with RNN. The Improved Coyote Optimization Algorithm (ICOA) improves both deep learners, and the conclusion of sentiment analysis result is considered both models. Thus, the suggested model surpasses standard methodologies regarding precision and accuracy, according to the results of the experiments. [ABSTRACT FROM AUTHOR]
Copyright of Cybernetics & Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Integrated Two Variant Deep Learners for Aspect-Based Sentiment Analysis: An Improved Meta-Heuristic-Based Model.
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  Data: Cybernetics & Systems; 2024, Vol. 55 Issue 7, p1631-1667, 37p
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  Data: <searchLink fieldCode="DE" term="%22CONVOLUTIONAL+neural+networks%22">CONVOLUTIONAL neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22RECURRENT+neural+networks%22">RECURRENT neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22OPTIMIZATION+algorithms%22">OPTIMIZATION algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22DEEP+learning%22">DEEP learning</searchLink><br /><searchLink fieldCode="DE" term="%22WEB+portals%22">WEB portals</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Several traditional methods were tested on standard datasets to evaluate client emotions transmitted via internet portals. Customers, on the other hand, continue to have difficulty obtaining aspect-oriented viewpoints voiced by other customers, and the accuracy of the current model is insufficient. The suggested Aspect-Based Sentimental Analysis (ABSA) starts with pre-processing, which includes "stop word and punctuation removal, lower case conversion, and stemming." Aspect extraction, which entails dividing the nouns and adjectives, as well as verbs and adverbs, is the following step. The weighted polarity features from the "Vader sentiment intensity analyzer, as well as the word2vector and Term Frequency-Inverse Document Frequency (TF-IDF)" are concatenated. OIDL stands for Optimized Integrated Deep Learning, which combines two types of deep learners. The first is the combination of concatenated features with "Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN)," while the second is the combination of concatenated features with RNN. The Improved Coyote Optimization Algorithm (ICOA) improves both deep learners, and the conclusion of sentiment analysis result is considered both models. Thus, the suggested model surpasses standard methodologies regarding precision and accuracy, according to the results of the experiments. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Cybernetics & Systems is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: 2024
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