BUILDING A CONTENT RECOMMENDATION FRAMEWORK FOR PERSONALIZING STREAMING EXPERIENCE USING SENTIMENT ANALYSIS - Sukriti singh , sujata joshi (PalArch’s Journal of Archaeology of Egypt / Egyptology, 2020)
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