Advanced Sentiment and Trend Analysis of Twitter Data Using CNN-LSTM and Word2Vec

This paper conducts an in-depth study on sentiment and trend analysis of Twitter data, employing a hybrid deep learning approach to better understand user behavior and engagement patterns. In an era where Twitter is a crucial platform for real-time information exchange and public discourse, analyzing the sentiment and trends in tweets provides vital insights for stakeholders such as marketers, policymakers, and researchers. The study integrates advanced feature extraction techniques, particularly Word2Vec, with a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. By focusing on tweets related to major global events—such as elections, natural disasters, and significant sports events—the research uncovers the underlying sentiment and tracks evolving trends in public opinion. The methodology involves extensive data preprocessing and the application of the CNN+LSTM model, which significantly outperforms traditional machine learning models. The proposed model, enhanced with Word2Vec features, achieves an accuracy of 92%, a precision of 91%, a recall of 89%, and an F1 score of 90%, highlighting its effectiveness in capturing the dynamic nature of Twitter data. The study also examines the influence of external factors on tweet sentiment and trend evolution, offering a detailed understanding of how public opinion shifts over time. These findings demonstrate the hybrid model's superiority in predictive performance and robustness, offering valuable insights for developing targeted engagement strategies, content curation, and real-time decision-making on Twitter. The research emphasizes the potential of advanced machine learning techniques to enhance social media analytics, contributing significantly to the broader field of natural language processing.