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ABSTRACT
Sentiment and emotional analysis have emerged as indispensable tools in understanding and interpreting the rich tapestry of human communication. This research delves into the intricate realm of natural language processing to decipher the sentiments and emotions embedded in textual data. The study employs advanced machine learning and deep learning techniques to extract nuanced insights from diverse sources such as social media, customer reviews, and textual data in general.
The methodology involves preprocessing textual data to handle challenges like noise, sarcasm, and ambiguity, followed by feature extraction to capture both explicit and implicit emotional cues. The research explores the synergy between sentiment analysis, which gauges the polarity of opinions, and emotional analysis, which discerns the underlying emotions like joy, anger, sadness, and more. The aim is to create a holistic framework that not only detects sentiment but also unravels the emotional nuances inherent in human expression.
