• Sanjana C Suvarna
• Shreya S Rao
• Sowjanya
• Yamini
• Suresha D

In today’s digital era, online streaming platforms have become a primary source of entertainment, but users often struggle to choose content that matches their current emotional state. This project introduces a Netflix-like application enhanced with a mood-based movie recommendation system using a chatbot interface. This paper details the development of a Netflix clone enhanced with a chatbot that provides mood-based movie suggestions. The platform replicates core Netflix functionalities, including browsing and video playback, while integrating an interactive chatbot. Users can articulate their current emotional state to the chatbot, which then leverages natural language processing and a curated movie database to recommend films aligning with that mood. This approach moves beyond conventional genre-based recommendations, offering a more personalized and intuitive way for users to discover content. The system architecture, chatbot implementation, and mood-to-movie mapping strategy are discussed. Preliminary evaluations suggest that the mood-based recommendations enhance user engagement and satisfaction by providing more contextually relevant viewing options. This project explores the potential of integrating conversational AI to improve content discovery in streaming platforms.

Netflix Clone, Chatbot, Mood Detection, Emotion-Based Recommendation, Movie Streaming, JavaScript, Web Application

"Netflix Clone with mood-based movie suggestions via chatbot ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.265-269, July-2025, Available :http://www.jetir.org/papers/JETIRGX06048.pdf

"Netflix Clone with mood-based movie suggestions via chatbot ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. pp265-269, July-2025, Available at : http://www.jetir.org/papers/JETIRGX06048.pdf