Link to Tableau Public Dashboard: https://public.tableau.com/views/TheLittleMermaidSentimentAnalysis/Dashboard1?:language=en-US&publish=yes&:display_count=n&:origin=viz_share_link

In this project, I conducted a sentiment analysis of user comments related to the movie "The Little Mermaid." The analysis aimed to understand the online community's sentiment and engagement surrounding this highly anticipated film release.

The data collection began by web scraping user comments from YouTube. I selected a prominent video related to "The Little Mermaid" and gathered a substantial number of comments. This step allowed me to obtain real-time user-generated content directly from a popular platform, providing valuable insights into the audience's reactions.

After collecting the comments, I performed sentiment analysis on the textual data. I classified each comment into one of three categories: positive, negative, or neutral. This analysis enabled me to gauge the overall sentiment and emotional tone of the audience's responses.

• Anticipation and Excitement : I observed a significant surge in user engagement, with a notably high number of comments being posted just prior to the theatrical launch of the movie on May 26, 2023. This heightened activity suggests that anticipation and excitement were building up among the audience in the lead-up to the release.

• Digital and Physical Releases : Notably, two additional peaks in user engagement were observed. The first occurred at the end of July, coinciding with the digital download release of "The Little Mermaid." The second, although smaller, corresponded to the release of the movie on Ultra HD Blu-ray, Blu-ray, and DVD in September. These peaks indicated ongoing user interest in different release formats.

• Sentiment Distribution : I found that the majority of user comments fell within the "Positive" sentiment category, accounting for 53.79% of all comments. A significant proportion (28.38%) were classified as "Neutral," indicating a considerable number of users provided comments that did not strongly express either positive or negative sentiment. "Negative" sentiment comments accounted for a smaller fraction, at 17.83%.

• Online Conversation : The analysis of word cloud visualizations highlighted keywords and topics frequently discussed by users. These terms ranged from references to the movie's characters and plot elements to sentiments like love, excitement, and disapproval.

In conclusion, the sentiment analysis of user comments related to "The Little Mermaid" unveiled valuable insights into user engagement and emotional responses. The data collected from YouTube comments, combined with sentiment analysis, provided a comprehensive understanding of the online community's sentiments and interactions surrounding this highly anticipated movie release.