Advanced Novel NLP Approach for Emotion Detection in Poetry

In the era of big data, characterized by the exponential growth of textual information across diverse domains, there arises a pressing need for sophisticated techniques in sentiment analysis. Serving as a vital component of natural language processing, sentiment analysis involves the extraction and comprehension of subjective information from textual data. This paper introduces an innovative and comprehensive framework for sentiment analysis, strategically designed to yield profound insights and provide decision support in the realm of textual data. The proposed framework harnesses the power of state-of-the-art machine learning and deep learning techniques, aiming to elevate both the accuracy and granularity of sentiment analysis. Adopting a multi-faceted approach, it seamlessly integrates traditional lexical-based methods with cutting-edge neural network architectures, enabling the model to adeptly capture subtle nuances and context-dependent sentiments. Additionally, the framework incorporates domain-specific lexicons and embeddings, tailoring sentiment analysis to the intricacies of diverse industries. To ensure practical applicability and scalability, the framework is implemented and rigorously evaluated across a spectrum of datasets encompassing social media, customer reviews, and industry-specific corpora. The results underscore the robustness of the framework in effectively handling disparate data sources and its remarkable adaptability to nuanced expressions of sentiment.