Sentiment Analysis in NLP: Techniques, Applications, and Future Directions

From rule-based systems to deep learning, the author presents everything you need to know about sentiment analysis As sentiment analysis evolves from simple lexicon matching to sophisticated multimodal deep learning, practitioners need authoritative guidance spanning the field's entire trajectory.Sentiment Analysis in NLP delivers this breadth, covering text-based, aspect-based, multimodal, and implicit sentiment analysis, integrating text, audio, and visual data processing while addressing both theoretical foundations and real-world implementations.This book examines neural network architectures including CNNs and RNNs for text analysis, transformer models like BERT, and Graph Attention Networks.Dedicated chapters cover attention mechanisms and generative AI for synthetic data generation.Practical applications span product development, social media monitoring, and public health surveillance.Python code, datasets, and a solutions manual support hands-on learning.Readers will also find: