For every point that the home team scores, you will have thousands of people praising the team. For every point that the home team allows, you will have thousands of people calling for the team to be sold. Regardless of the sport, fans can be very polarizing. It is this poralization that had me wondering, "are any fan bases happy?" Taking this a step further, I asked, "Which team is the most optimistic and positive out of all NFL fan bases". Using hundreds of thousands of real Reddit comments, this project uses sentiment analysis to try to answer the latter question.

Before we could begin, we needed to better refine the question that we wanted to answer. What does it mean for a team to be positive? What does it mean for a team to be negative? How are we going to collect the data to begin with?

For this project, I decided to focus on one question, "What is the most optimistic fan base in the NFL?" To determine this answer, the plan was to get an average sentiment of how the overall fan base was feeling after a loss. The reason I decided to go with this is because it's easy for spirits to be high and for people to be happy after a win, however, for people to still be positive even after a loss shows resilience, hope, and optimism.

To encapsulate our plan in one sentence, we are trying to determine average sentiment after a loss.

Now that we have the question, it's time to actually get the data. For this part, there are two pieces that we need. The first data we need is simply team results. To keep it simple, I went ahead and collected the result of each teams match for the 2025-26 NFL season.

The next bit of data was the tricky part.

I needed to find a reliable way to gather sentiments for each team after every game. Naturally, my mind deviated towards Reddit. During any game, you will find that each teams subreddit will have a Game Time Thread and a Post Game Thread. During Live Game Threads (LGM), users will leave comments on the thread reacing to events as they happen in live time. After a game concludes, a Post Game Thread (PGT) appears. It is in this PGT that users take the time to give their immediate reactions after a game. While Live Game Threads tend to have thousands more comments compared to Post Game Threads, Post Game Threads are going to be more valuable for our analysis. For this to work, I need to collect every single GDT from every team. That way, I can write some code using the Reddit API to scrape the comments. The issue is, it's not as simple as writing a script that visits every teams subreddit, finds the PGT, and then stores the URL somewhere. There is no consistent way that all 32 teams present the PGTs. This is to say that for this part, I manually visited all 32 subreddits and found all 17 PGTs for each team.

I had to manually locate 544 Reddit threads. I'm sure I could've optimized it but with how inconsistent some of the PGT titles were, I figured that I would just invest the time to ensure that all 544 threads were correct.

Once I felt good about the URLs, we could finally start using Python to speed the process up a bit. Using the official Reddit API, I was able to write a short script that would be able to take the URLs and scrape all of the comments from each PGT. This is where a bit of nuance comes in to play. To ensure that the data was "clean", I could only take the parent comments. Let me give an example as to why this is the case.

Let's pretend that I'm going to scrape every single comment.

User 1: This team sucks.

User 2: Reply to User 1: you suck.

With these two lines alone, you might already see the issue. While User 2 has a comment with negative sentiments, those sentiments are not directed at the team. Instead, they're targeted at another user. Innerfandom toxicty aside, this nuance is a bit difficult for a model to distinguish. To keep things simple and save a bit of time, I made the decision to only keep the parent comments. We can hopefully assume that all parent comments are directed at the team itself.