Published Paper Details:

Sentiment analysis, a fundamental task in natural language processing (NLP), aims to determine the emotional tone conveyed in textual data, providing valuable insights into user opinions and preferences. With the rapid expansion of online platforms and the proliferation of customer feedback, businesses increasingly rely on automated sentiment classification to enhance decision-making and customer engagement.This study explores the potential of deep learning in sentiment analysis by employing Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to analyze sentiment in Amazon product reviews. The research leverages a large dataset of Amazon reviews, encompassing diverse product categories and varying sentiment intensities, to develop a robust classification model. The preprocessing pipeline includes text cleaning, tokenization, stopword removal, stemming, and vectorization to convert raw textual data into a structured format suitable for model training. Feature extraction is performed using techniques such as word embeddings (Word2Vec, GloVe, or FastText), ensuring that the deep learning models capture contextual relationships and semantic nuances within the text.The CNN model, known for its ability to detect local patterns and hierarchical features in text data, demonstrates a test accuracy of 90.66%, while the RNN model, capable of learning sequential dependencies and long-term relationships in textual sequences, achieves a test accuracy of 90.82%. These results underscore the effectiveness of deep learning in sentiment classification, with CNN excelling in computational efficiency and RNN offering superior sequential processing capabilities. The study also considers hybrid architectures, such as Long Short-Term Memory (LSTM) networks and Bidirectional Gated Recurrent Units (Bi-GRU), to enhance performance further.Beyond technical implementation, this research highlights the growing significance of automating large-scale sentiment analysis in the era of digital transformation. Businesses can harness AI-driven sentiment classification to analyze consumer feedback, improve product recommendations, predict market trends, and enhance customer satisfaction. The ability to extract meaningful insights from vast volumes of customer reviews provides a competitive advantage, enabling companies to make data-driven decisions, refine marketing strategies, and foster stronger customer relationships.This study contributes to the evolving landscape of AI-powered sentiment analysis, paving the way for scalable and real-time applications in e-commerce analytics, social media monitoring, and customer experience management. Future research directions include integrating attention mechanisms, transformer-based architectures like BERT or GPT, and domain-specific fine-tuning to further optimize sentiment classification models and improve interpretability.