• Samiksha Malage
• Aayush Rajendra Pawar
• Asst. Prof. Gauri Mhatre

The Growth of WhatsApp as a primary communication platform has led to a surge in spam messages, posing significant risks to user privacy and security. The complicated and dynamic nature of spam on WhatsApp, where messages are frequently informal and diversified and include slang, abbreviations, and multimedia features, makes traditional rule-based spam detection algorithms inadequate. This study investigates the creation of a sophisticated machine learning-based system intended to examine WhatsApp conversations and efficiently identify spam. Utilizing Natural Language Processing (NLP) techniques, the purpose of this research is to develop a strong spam detection algorithm that can handle WhatsApp messaging's complexities. Data collection from WhatsApp conversations, preprocessing to clean and normalize the text data, feature extraction to identify crucial components indicating of spam, and training of a variety of machine learning models, such as Naive Bayes, Support Vector Machines (SVM), and sophisticated deep learning models like Long Short-Term Memory (LSTM) networks and Bidirectional Encoder Representations from Transformers (BERT), are some of the crucial steps in the methodology.

Whatsapp chat data, Analysis, Sentiment Analysis, NLP, Spam detection, Features and models.

"WHATSAPP CHAT ANALYZER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.e754-e765, June-2024, Available :http://www.jetir.org/papers/JETIR2406498.pdf

"WHATSAPP CHAT ANALYZER", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. ppe754-e765, June-2024, Available at : http://www.jetir.org/papers/JETIR2406498.pdf