Sentiment Analyzer
This project is an exploration into sentiment analysis, aiming to construct a resilient sentiment analyzer through natural language processing (NLP), highlighting the need for ongoing training on evolving datasets and the integration of advanced NLP models to elevate accuracy levels.
Abstract
Abstract: This project is an exploration into sentiment analysis, aiming to construct a resilient sentiment analyzer through natural language processing (NLP). The primary objective lies in identifying emotions—positive, negative, or neutral—in varied textual content. Methodologically, it involves the creation of meticulously curated datasets, employing advanced pre-processing techniques, and delving into diverse model explorations. Despite challenges encountered, such as deciphering sarcasm and navigating contextual nuances, the project achieves the development of a high-performing sentiment analyzer. Emphasizing continual enhancement, the project underscores the need for ongoing training on evolving datasets and the integration of advanced NLP models to elevate accuracy levels, acknowledging the significance of these advancements in sentiment analysis.