Review Paper on Transformer-based Generative Adversarial Network (GAN) hybrid model

Computer Engineering

Sentiment analysis (SA) is a core task in Natural Language Processing (NLP) that identifies opinions, emotions, and attitudes from text. Traditional approaches relied on machine learning and lexicon-based methods, but recent advances in deep learning—especially Transformers and Generative Adversarial Networks (GANs)—have significantly improved performance.Recent research focuses on hybrid architectures, combining Transformers’ contextual understanding with GANs’ generative and adversarial learning capabilities. Traditional automated feature selection methods face limitations, including increased computational costs as the number of features grows. Deep learning, with its ability to self-learn and efficiently process large datasets, helps address these challenges. This paper proposes using a conditional generative adversarial network (GAN) for sentiment analysis on Twitter, with a convolutional neural network (CNN) employed to extract features from the data.Sentiment analysis, which focuses on extracting insights from textual data to reveal people's opinions or attitudes on a specific issue, has become a prominent area of research in natural language processing (NLP), especially with the rise of social media. Twitter, as a popular platform, allows users to openly share their views and thoughts. However, analyzing Twitter data presents unique challenges due to the frequent use of slang, abbreviations, and misspellings in its short-form content.

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