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<p>The desire for comprehending user behavior is significant, since social media data is growing quickly owing to user contributions, especially in light of the recent coronavirus outbreak. In the literature works, several semantic analysis methodologies are used for examining the user behavior and emotions from the corpus data. They continue to deal with the main issues of poor accuracy, a complex system, inadequate emotion recognition, and ineffectiveness. The main objective of this paper is to build a new model for sentiment analysis and opinion mining. When compared to the conventional works, the novel concept behind this work is, it adopts some unique computational methodologies for accurate sentiment prediction. Here, a new Cycle Generative Adversarial Network (GAN) based Term Memory integrated Sentiment Analysis (CyG-TMSA) framework is developed for analyzing and classifying user emotions from corpus data. The most accurate identification of emotions is made possible by the efficient generation of augmented data using the Cycle Generative Adversarial Network (GAN) model. Moreover, a classification technique based on Term Memory Allocation (TMA) is applied to accurately predict opinions derived from the data supplementation. The best way to approximate the weight value for classifying data is to use the Elite Golden Jackle Optimization (EGJO) technique, which enhances the classifier’s decision-making ability. To verify and contrast the effectiveness and results of the suggested CyG-TAMS model, multiple corpus data sets extracted from public repositories are employed. The proposed CyG-TMSA framework demonstrated great effectiveness in sentiment analysis via CycleGAN-based data augmentation, TMA for precise classification, and optimization of parameter tuning through GJO. In experimental evaluations conducted on datasets extracted from Amazon and Twitter, the model obtained high accuracy at 98% within a very short processing time of 10.12&#xa0;s, significantly outclassing other conventional methods in terms of accuracy and computational efficiency.</p>

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A Radical Cycle GAN Based Term Memory Integrated Sentiment Analysis (CyG-TMSA) Framework for Social Media Applications

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月16日

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  • A. N. Arularasan,
  • P. Ganeshkumar,
  • Tahani Albalawi,
  • Mohammad Alkhatib

摘要

The desire for comprehending user behavior is significant, since social media data is growing quickly owing to user contributions, especially in light of the recent coronavirus outbreak. In the literature works, several semantic analysis methodologies are used for examining the user behavior and emotions from the corpus data. They continue to deal with the main issues of poor accuracy, a complex system, inadequate emotion recognition, and ineffectiveness. The main objective of this paper is to build a new model for sentiment analysis and opinion mining. When compared to the conventional works, the novel concept behind this work is, it adopts some unique computational methodologies for accurate sentiment prediction. Here, a new Cycle Generative Adversarial Network (GAN) based Term Memory integrated Sentiment Analysis (CyG-TMSA) framework is developed for analyzing and classifying user emotions from corpus data. The most accurate identification of emotions is made possible by the efficient generation of augmented data using the Cycle Generative Adversarial Network (GAN) model. Moreover, a classification technique based on Term Memory Allocation (TMA) is applied to accurately predict opinions derived from the data supplementation. The best way to approximate the weight value for classifying data is to use the Elite Golden Jackle Optimization (EGJO) technique, which enhances the classifier’s decision-making ability. To verify and contrast the effectiveness and results of the suggested CyG-TAMS model, multiple corpus data sets extracted from public repositories are employed. The proposed CyG-TMSA framework demonstrated great effectiveness in sentiment analysis via CycleGAN-based data augmentation, TMA for precise classification, and optimization of parameter tuning through GJO. In experimental evaluations conducted on datasets extracted from Amazon and Twitter, the model obtained high accuracy at 98% within a very short processing time of 10.12 s, significantly outclassing other conventional methods in terms of accuracy and computational efficiency.