A SURVEY ON SMART INTELLIGENCE POWERED TWITTER SENTIMENT ANALYSIS BASED ON DEEP FEATURE ENGINEERING WITH HYPER SCALED SENTIMENT LSTM GATED CNN

Abstract I Dr.J.Jebathangam Professor, Department of Computer Applications, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Chennai, Tamil Nadu, India. n recent years, Twitter has become a significant platform for public discourse, opinion sharing, and real-time reactions to global events. Extracting sentiment from tweets is crucial for applications in marketing, politics, healthcare, and crisis management. However, analyzing Twitter data presents unique challenges such as informal language, sarcasm, brevity, and high volume. Traditional machine learning techniques fall short in capturing these complexities, whereas deep learning has emerged as a powerful alternative. This survey provides a comprehensive overview of Twitter sentiment analysis with a focus on deep learning methodologies, especially hybrid models combining Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN). We introduce the concept of Hyper Scaled Sentiment LSTM Gated CNN, which leverages deep feature engineering, gating mechanisms, and layer scaling techniques to enhance model performance and generalization on noisy data. The paper reviews essential preprocessing techniques, benchmark datasets, and evaluation metrics while analyzing current trends and innovations in the field. Finally, we discuss limitations, open challenges, and promising future research directions including explainability, multilingual sentiment detection, and multimodal fusion. Keywords: Twitter Sentiment Analysis, Deep Learning, LSTM, CNN, Feature Engineering, Gated Networks, Hyper Scaling, Natural Language Processing, Social Media Mining, Text Classification.