Analyzing Sentiment in Netflix User Opinions: A Statistical Examination

The dataset encompasses Netflix’s extensive collection of movies and TV shows, reflecting its prominent position as a leading global streaming platform with over 8000 titles and 200 million subscribers by mid-2021. Employing sentiment analysis using machine learning techniques, the research delves into user opinions extracted from textual data. Key methodologies include Natural Language Processing (NLP) for sentiment scoring and multinomial logistic regression for predicting sentiment categories based on factors like ‘Score’ and ‘Rating’. Evaluation metrics reveal the model’s exceptional accuracy and reliability in classifying sentiments, particularly in identifying positive sentiments influenced by user ratings. These findings hold implications for content curation strategies, emphasizing the importance of factors contributing to positive sentiment ratings in the digital entertainment landscape.