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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63128
Volume:
10 |
5 |
May 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Master of Computer Applications
G H Raisoni College of Engineering & Management, Wagholi, Pune
Master of Computer Applications
G H Raisoni College of Engineering & Management
Master of Computer Applications
G H Raisoni College of Engineering & Management, Wagholi, Pune
In recent years, conversational artificial intelligence (AI) has evolved from simple rule-based systems to interactive assistants capable of understanding user context and emotions. However, many chatbot frameworks rely on complex machine learning models and cloud infrastructure, making them resource-intensive for lightweight applications. This study proposes MoodMate, a Python-based mood-aware chatbot that provides personalized recommendations for movies, music, and motivational content. The system uses a keyword-based sentiment detection method to identify emotions such as happiness, sadness, or boredom. The modular architecture ensures efficient performance with low computational requirements and fast response times. The proposed system demonstrates how lightweight conversational AI can deliver personalized experiences while remaining accessible and scalable for educational and wellness applications.
KEYWORDS
In this research, an emotion-aware conversational system MoodSense: A Python-Based Emotion Detection Chatbot for Personalized Entertainment Recommendations has been designed and explained. The main objective of this system is to detect the emotional state of users and provide suitable entertainment suggestions that can improve their mood and enhance user engagement. The system analyzes user messages and identifies emotions using a keyword-based emotion detection approach. Instead of storing complex models, the chatbot processes the user input through predefined emotion categories such as happiness, sadness, boredom, and stress. This lightweight method allows the system to quickly recognize the user’s emotional state and generate appropriate responses. The chatbot integrates several external APIs to provide dynamic recommendations. Movie suggestions are obtained through the TMDb API, music recommendations are generated using the Spotify API, and motivational quotes are retrieved from the ZenQuotes API. Additionally, weather data from the OpenWeatherMap API helps the chatbot provide context-aware suggestions based on environmental conditions. The testing results show that the system responds effectively to user inputs and provides relevant entertainment recommendations. MoodSense demonstrated stable performance and quick response time during interactions. Although the keyword-based emotion detection approach has certain limitations in understanding complex language expressions, the system still offers a practical and engaging conversational experience. In conclusion, MoodSense demonstrates how a Python-based chatbot can provide personalized entertainment recommendations through simple emotion detection techniques. The system highlights the potential of conversational AI to create interactive, mood-aware digital assistants that enhance user experience and digital well-being.
[1] Jurafsky Daniel and James H. Martin, Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Pearson Education, 2009. [2] Python Software Foundation. Python Language Reference Manual. Available: https://www.python.org [3] Spotify for Developers. Spotify Web API Documentation. Available: https://developer.spotify.com/documentation/web-api/ [4] The Movie Database. TMDb API Documentation. Available: https://developer.themoviedb.org [5] OpenWeather. OpenWeatherMap API Guide. Available: https://openweathermap.org/api [6] Requests Python Library. Requests: HTTP for Humans Documentation. Available: https://docs.python-requests.org/ [7] Beautiful Soup Library Documentation. Beautiful Soup for Web Scraping. Available: https://www.crummy.com/software/BeautifulSoup/
Publication Date
5/13/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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