• Suraj Jha
• Rushikesh Wade
• Rohit Baviskar
• Akash Tuli
• Prof. Mrs. Chandani Lachke

Detecting fraudulent behavior is an enormous problem within the digital era, given the great use of on-line transactions that have created several opportunities for deceptive practices. This initiative introduces an imaginative strategy to deal with the issue of fraud detection, making use of sentiment analysis within the realm of monetary dealings. The predominant intention is to create a fraud detection software that makes use of sentiment evaluation to pinpoint doubtlessly deceitful sports, relying on the emotional undercurrent and linguistic systems discovered in transaction-related textual statistics. The fundamental operations of the application center on sentiment evaluation, entailing the extraction and examination of sentiments conveyed in text related to economic transactions. By implementing Natural Language Processing (NLP) methodologies, the software will categorize those sentiments as either superb, negative, or neutral. Moreover, the sentiment evaluation will delve deeper than mere polarity classification, aiming to come across subtler emotional complexities like unease, enthusiasm, or ambiguity.

Fraud Apps, Sentiment Analysis, Detection, Sentiment evaluation, Natural Language Processing (NLP), Preprocessing.

"Fraud App Detection Using Sentiment Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 1, page no.a602-a604, January-2024, Available :http://www.jetir.org/papers/JETIR2401074.pdf

"Fraud App Detection Using Sentiment Analysis", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 1, page no. ppa602-a604, January-2024, Available at : http://www.jetir.org/papers/JETIR2401074.pdf