Mitigating BHP Flood Attacks in OBS Networks: A Machine Learning-Based Approach Using CNNs and RNNs

Abstract

Sentiment analysis on social media text has garnered significant attention due to its applications in understanding public opinion and sentiment trends. This paper conducts a comparative analysis of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for sentiment analysis tasks on social media data. We evaluate their performance using popular datasets such as Sentiment140 and Twitter datasets, focusing on accuracy, computational efficiency, and robustness to noisy and informal language. Experimental results demonstrate the strengths and limitations of CNNs and RNNs in capturing sentiment from diverse textual data sources. Additionally, we discuss the implications of our findings for enhancing sentiment analysis methodologies and applications in real-world scenarios. This study contributes insights into selecting suitable architectures for sentiment analysis tasks on social media, thereby aiding researchers and practitioners in leveraging advanced NLP techniques effectively.