Volume 8 • Issue 2 • PP: 55-62 • 2024
Visual Harmony Tailoring Video Recommendations through Text
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This research develops a novel approach for mood-based YouTube video suggestions. Using cutting-edge textual data analysis techniques, through the application of Natural Language Processing (NLP) techniques combined with sentiment analysis based on the FrameNet framework, users' everyday experiences and feelings are carefully analyzed to determine their current mood in the text. The process of content curation is made easier by the extraction of pertinent video metadata with the help of the YouTube API key. The integration of video metadata with textual mood extraction allows for the development of an extremely engaging and personalized content recommendation system. Users are provided with content that resonates with their current emotional state by matching the recommended movies' mood with the one deduced from the textual input. This improves user satisfaction and enriches their experience.
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References
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