Applying LSTM networks for enhanced sentiment analysis in social media monitoring

Abstract

 The rapid proliferation of social media platforms has led to an unprecedented surge in usergenerated content, necessitating advanced analytical techniques to decode public sentiment accurately. This manuscript explores the application of Long Short-Term Memory (LSTM) networks to enhance sentiment analysis in social media monitoring. By leveraging the inherent ability of LSTMs to capture long-range dependencies in text data, the proposed framework is designed to overcome challenges posed by the noisy and unstructured nature of social media language. The study details the preprocessing steps, model architecture, and evaluation methodologies employed to assess performance improvements over traditional sentiment analysis methods. Experimental results, based on datasets comprising tweets, Facebook posts, and forum discussions, demonstrate that the LSTM-based approach achieves superior accuracy and robustness, particularly in handling context-dependent expressions and sentiment shifts. The findings underline the potential of LSTM networks to serve as a core component in sophisticated social media monitoring systems, offering valuable insights for businesses, policymakers, and researchers interested in real-time sentiment analysis.