BUILDING A CONTENT RECOMMENDATION FRAMEWORK FOR PERSONALIZING STREAMING EXPERIENCE USING SENTIMENT ANALYSIS

Authors

  • Sukriti singh , sujata joshi

Abstract

The success in the OTT(Over-the-top) industry depends on two important factors i.e.
User engagement and subscriber's satisfaction percentage. The objective of this research is to
propose a content recommendation framework that will help OTT players in personalizing the
streaming experience and identify what content they should stream for more viewership
The secondary data is collected from Twitter, Facebook, websites, etc. Observational
analysis approach was followed, and sentiment analysis was used to analyze text conversations
and evaluates the tone, intent, and emotion behind messages. Tools used to run Sentiment
Analysis: Anaconda (Jupyter Notebook), Python, Excel is used to find the correlation.
This framework will help companies that offer online streaming services to get an insight
into consumer preference of content and provide a tailored streaming experience. The framework
will not only be useful to video streaming services but also news, music streaming apps to
increase their profitability and meet business objectives

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Published

2020-12-01

How to Cite

Sukriti singh , sujata joshi. (2020). BUILDING A CONTENT RECOMMENDATION FRAMEWORK FOR PERSONALIZING STREAMING EXPERIENCE USING SENTIMENT ANALYSIS. PalArch’s Journal of Archaeology of Egypt / Egyptology, 17(6), 4727 - 4742. Retrieved from http://mail.palarch.nl/index.php/jae/article/view/1720