Sentiment Analysis With Lipschitz Recurrent Neural Networks Based Generative Adversarial Networks

This paper applies a novel sentiment analysis method to the IMDB movie review dataset by integrating Lipschitz Recurrent Neural Networks (LRNN) into a Generative Adversarial Network (GAN) architecture. Traditional sentiment analysis techniques frequently struggle with instability and disappearing gradients during model training. Our method reduces these problems by using LRNN's stability and effectiveness, improving sentiment analysis's precision and resilience. The GAN framework efficiently synthesizes and discriminates between real and generated sentiment-laden textual data. It consists of a generator and discriminator that are both equipped with LRNN. The discriminator distinguishes between real and fake data and rates the sentiment correctness of the generated text, while the generator concentrates on producing realistic, sentiment-laden prose. A typical issue in traditional GAN models, the gradient vanishing problem is addressed by integrating LRNN in both the generator and discriminator, stabilizing the training process and enhancing performance. With an accuracy of 91.30 % on the IMDB dataset, our results significantly improve sentiment analysis accuracy, surpassing some well-known models. This indicates that LRNN-based GAN models can effectively handle challenging sentiment analysis tasks. Subsequent investigations will delve into the utilization of this paradigm in industry-specific settings like healthcare, banking, and education, as well as its incorporation into chatbot platforms. This work advances sentiment analysis techniques by providing a reliable and effective method for analyzing sentiments in big datasets.