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Journal of Cognitive Human-Computer Interaction

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Journal of Cognitive Human-Computer Interaction

Volume 8 , Issue 2 , PP: 55-62, 2024 | Cite this article as | XML | Html | PDF | Full Length Article

Visual Harmony Tailoring Video Recommendations through text

Jayakaran P. 1 , Litheeswaran S. 2 , Janakiraman S. 3 , Manikandan 4 , S. Malathi 5

  • 1 Undergraduate students, Department of Artificial Intelligence and Data Science, Panimalar Engineering College. - (jayakaranvicky56@gmail.com)
  • 2 Undergraduate students, Department of Artificial Intelligence and Data Science, Panimalar Engineering College. - (litheez10@gmail.com)
  • 3 Undergraduate students, Department of Artificial Intelligence and Data Science, Panimalar Engineering College. - (janakiraman1619@gmail.com)
  • 4 Undergraduate students, Department of Artificial Intelligence and Data Science, Panimalar Engineering College. - (manikandanvk2023@gmail.com)
  • 5 Department of Artificial Intelligence and Data Science, Panimalar Engineering College. - (adshod@panimalar.ac.in)

    • Doi: https://doi.org/10.54216/JCHCI.080206

    Received: October 22, 2023 Revised: January 12, 2024 Accepted: May 19, 2024
    Abstract

    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.

     

    Keywords :

    FrameNet , YouTube API Key , Natural Language Processing

      ,

    References

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    Cite This Article As :
    P., Jayakaran. , Litheeswaran, . , S., Janakiraman. , , Manikandan. , Malathi, S.. Visual Harmony Tailoring Video Recommendations through text. Journal of Cognitive Human-Computer Interaction, vol. , no. , 2024, pp. 55-62. DOI: https://doi.org/10.54216/JCHCI.080206
    P., J. Litheeswaran, . S., J. , M. Malathi, S. (2024). Visual Harmony Tailoring Video Recommendations through text. Journal of Cognitive Human-Computer Interaction, (), 55-62. DOI: https://doi.org/10.54216/JCHCI.080206
    P., Jayakaran. Litheeswaran, . S., Janakiraman. , Manikandan. Malathi, S.. Visual Harmony Tailoring Video Recommendations through text. Journal of Cognitive Human-Computer Interaction , no. (2024): 55-62. DOI: https://doi.org/10.54216/JCHCI.080206
    P., J. , Litheeswaran, . , S., J. , , M. , Malathi, S. (2024) . Visual Harmony Tailoring Video Recommendations through text. Journal of Cognitive Human-Computer Interaction , () , 55-62 . DOI: https://doi.org/10.54216/JCHCI.080206
    P. J. , Litheeswaran . , S. J. , M. , Malathi S. [2024]. Visual Harmony Tailoring Video Recommendations through text. Journal of Cognitive Human-Computer Interaction. (): 55-62. DOI: https://doi.org/10.54216/JCHCI.080206
    P., J. Litheeswaran, . S., J. , M. Malathi, S. "Visual Harmony Tailoring Video Recommendations through text," Journal of Cognitive Human-Computer Interaction, vol. , no. , pp. 55-62, 2024. DOI: https://doi.org/10.54216/JCHCI.080206

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