ABSTRACT

The rapid growth of online platforms, such as social media, e-commerce websites, and news portals, has resulted in an unprecedented amount of user-generated content. This content is rich with opinions, emotions, and sentiments that, when analyzed, can provide valuable insights into public opinion, consumer behavior, and trends. However, the sheer volume of this data poses significant challenges for traditional sentiment analysis techniques, which often struggle with issues like contextual understanding, sarcasm detection, and processing large-scale datasets.  This project explores the application of Natural Language Processing (NLP) in Sentiment Analysis to address these challenges. Sentiment Analysis, a subfield of NLP, is concerned with the computational study of opinions, sentiments, and emotions expressed in text. The primary aim of this project is to design, develop, and implement a sentiment analysis system that can automatically classify the sentiment of textual data into categories such as positive, negative, or neutral. The system leverages advanced machine learning techniques, including deep learning models and word embedding’s, to enhance the accuracy of sentiment classification. The implementation of these techniques enables the system to capture the nuances of human language, including context and sentiment expressed through complex linguistic structures. This project contributes to the field of NLP and sentiment analysis by presenting a solution for analyzing textual data. The report concludes with a discussion on the limitations of the current system and recommendations for future research, including the potential integration of multilingual support and real-time sentiment analysis capabilities.