• T S Bhagavath Singh
• Akshatha S Rao
• B Sri Harsha
• Charan P
• Disha Bhat

The capacity to effectively extract important in- formation is essential in the fast-paced world of today. This demand is met by Brevity AI, a state-of-the-art natural language processing (NLP) model that summarizes long texts while maintaining their essential content. This study explores how to improve Brevity AI so that it can identify the polarity of emotions. The resulting summary can transmit both factual information and the emotional tone of the original text by assessing the mood represented in it. This capability is especially helpful in domains where knowing the underlying emotion can yield insightful information, such as content analysis, market research, and customer service. The methodology used to incorporate emotion polarity detection into Brevity AI is described in the study. This involves applying pre-trained sentiment analysis models and refining techniques to ensure both the accuracy and relevance of the summaries that are generated, maintaining their precision and practical applicability. Brevity AI is a more flexible and educational tool for text summarizing as a consequence of experimental results demonstrating the effectiveness the improved model which captures both factual content and emotional nuances

Brevity AI, NLP, Emotion Polarity Detection, Text Summarization, Sentiment Analysis, Pre-trained models

"Brevity AI using NLP and Emotion Polarity Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 12, page no.a564-a568, December-2024, Available :http://www.jetir.org/papers/JETIR2412049.pdf

"Brevity AI using NLP and Emotion Polarity Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 12, page no. ppa564-a568, December-2024, Available at : http://www.jetir.org/papers/JETIR2412049.pdf