Natural Language Processing (NLP) has emerged as a pivotal technology in bridging the gap between human communication and computational systems. This research explores the development and application of NLP techniques to analyze, understand, and generate human language. Unlike rule-based systems, modern NLP leverages machine learning and deep learning to handle linguistic nuances, enabling applications such as sentiment analysis, machine translation, and chatbots. To demonstrate the practicality of NLP, we utilized the IMDB movie review dataset for sentiment analysis, employing techniques like tokenization, word embeddings, and recurrent neural networks (RNNs). The system was evaluated using metrics such as accuracy, precision, and recall to ensure robustness. Additionally, we developed an interactive dashboard using Streamlit to visualize sentiment trends and allow users to input custom text for real-time analysis. This dashboard facilitates engagement with non-technical stakeholders, promoting data-driven insights. Our results highlight the system’s ability to classify sentiment with high accuracy and adapt to diverse linguistic patterns. By addressing challenges such as ambiguity and data scarcity, this research contributes to the broader goal of enhancing human-computer interaction through intelligent, scalable NLP system