Analyzing the Public Feedback on Uncut Gems via Twitter

In this article, we'll explore the sentiment analysis of the popular crime/thriller film, Uncut Gems, on Twitter using Python libraries like Tweepy, textblob, seaborn, and matplotlib.

Uncut Gems, a film ten years in the making, was written and directed by Josh and Benny Safdie. The movie stars Adam Sandler, Julia Fox, Lakeith Stanfield, and former NBA player Kevin Garnett. Despite mixed reviews from the author's peers, the film has been a commercial success, becoming A24's highest grossing release, bringing in $40 million at the time of writing.

To begin, the author applied for a Twitter developer account to use Tweepy, a free Python library for accessing the Twitter API. Next, they collected tweets about Uncut Gems and its starring actors to demonstrate more positive sentiment than negative sentiment. To remove the potential influence of the word "Uncut" in the movie title on the sentiment scores, the author removed this word from tweets before analysis.

The author then used the python package textblob to get sentiment scores. The polarity score, a measure for positive or negative sentiment, was used, with values ranging from -1 to +1. The author demonstrated how to get sentiment polarity scores for tweets about "Uncut Gems" and stored them in a data frame.

However, the author noticed a flaw in using textblob, where it puts a heavier weight on the presence of negative words, which can inflate false negatives. To counteract this, the author removed the word "Uncut" from tweets to generate sentiment scores.

The author also counted the number of positive and negative sentiments and visualized the results using libraries seaborn and matplotlib. Interestingly, the presence of the word "Uncut" in the movie title significantly reduced the sentiment value.

To carry out sentiment analysis on tweets about celebrities like Adam Sandler, Julia Fox, Lakeith Stanfield, Kevin Garnett, and the Safdie Brothers, the author wrapped the process in a function for code reuse.

Finally, the author will continue to analyze the sentiment of tweets about Uncut Gems and its cast to gain a better understanding of the film's impact and reception on social media.

If you're interested in this project, the author's code from this post is available on GitHub, and small contributions can be made through the author's Patreon. For more information on textblob, please visit here.