Madison Square Garden vs Chase Center

Author

Ben Coyle

Introduction

Every arena across the NBA is different in their own way. Some arenas have different color courts, some arenas are bigger and some are louder. I want to compare the oldest arena ever (Madison Square Garden) with the newest arena (Chase Center) to see how their reviews compare to each other. I used tripadvisor.com to compare the reviews.

library(tidyverse)

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library(tidytext)

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library(ggwordcloud)

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library(textdata)

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library(readr)
library(rvest)

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library(lubridate)
library(httr)

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library(stringr)
library(ggplot2)
library(knitr)
arena_stats <- read.csv("https://myxavier-my.sharepoint.com/:x:/g/personal/coyleb2_xavier_edu/Ed8jUQqCzI9BgpEdY9t8RD0BwMJzlGFDBwk-rehS1n824g?download=1")

Madison Square Garden vs Chase Center Net Sentiment Comparison

#Unnest tokens
words <- arena_stats %>%
  unnest_tokens(word, review_content)

# Get Bing sentiment lexicon
bing_lexicon <- get_sentiments("bing")

# Join with Bing sentiments
word_sentiments <- words %>%
  inner_join(bing_lexicon, by = "word")

# Count positive and negative sentiments for each arena
sentiment_counts <- word_sentiments %>%
  count(arena, sentiment) %>%
  spread(sentiment, n, fill = 0) %>%
  mutate(total = positive + negative, net_sentiment = positive - negative)

# Filter for Madison Square Garden and Chase Center
filtered_arenas <- sentiment_counts %>%
  filter(arena %in% c("Madison Square Garden", "Chase Center"))

# Compare sentiments
print(filtered_arenas)

                  arena negative positive total net_sentiment
1          Chase Center       30       54    84            24
2 Madison Square Garden       45       61   106            16

ggplot(filtered_arenas, aes(x = arena, y = net_sentiment, fill = arena)) +
  geom_bar(stat = "identity") +
  theme_minimal() +
  labs(title = "Net Sentiment Comparison: Madison Square Garden vs Chase Center",
       x = "Arena", y = "Net Sentiment Score")

Looking at the table and graph above, Madison Square Garden seem to have more positive and negative reviews. This makes sense because since this arena is so historic, people have strong opinions. Overall, Chase Center has a higher net sentiment which is good because since Chase Center has opened, people have been giving it positive reviews.

Sentiment Over Time

bing_lexicon <- get_sentiments("bing")
arena_sentiments <- arena_stats %>%
  filter(arena %in% c("Madison Square Garden", "Chase Center")) %>%
  unnest_tokens(word, review_content) %>%
  inner_join(bing_lexicon, by = "word") %>%
  mutate(review_date = mdy(review_date)) %>%
  group_by(arena, review_date, sentiment) %>%
  summarize(count = n(), .groups = 'drop')

Warning: There was 1 warning in `mutate()`.
ℹ In argument: `review_date = mdy(review_date)`.
Caused by warning:
!  84 failed to parse.

# Calculate sentiment score (positive - negative)
sentiment_scores <- arena_sentiments %>%
  spread(sentiment, count, fill = 0) %>%
  mutate(sentiment_score = positive - negative)

# Plotting sentiment over time
ggplot(sentiment_scores, aes(x = review_date, y = sentiment_score, color = arena)) +
  geom_line() +
  labs(title = "Sentiment Over Time for Madison Square Garden",
       x = "Review Date", y = "Sentiment Score") +
  theme_minimal()

Warning: Removed 1 row containing missing values (`geom_line()`).

For this graph, I only looked at Madison Square Garden’s sentiment over time because the chase center is so new. I realized that Madison Square Garden’s sentiment over time has a lot to do with the Knicks performance. They were disappointing in 2021 and 2022 and are now getting back on track. In 2020 their sentiment was probably so high because not many people could review MSG during the corona virus.

Word Analysis

arenastats_filtered <- arena_stats %>%
  filter(arena %in% c("Madison Square Garden", "Chase Center")) %>%
  unnest_tokens(word, review_content) %>%
  anti_join(stop_words, by = "word")  # Remove common stop words

# Count word frequencies
word_counts <- arenastats_filtered %>%
  count(arena, word, sort = TRUE) %>%
  group_by(arena) %>%
  top_n(10, n)  # Get top 10 words for each arena

# Plotting
ggplot(word_counts, aes(x = reorder(word, n), y = n, fill = arena)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~arena, scales = "free_y") +
  coord_flip() +
  theme_minimal() +
  labs(title = "Top Words in Reviews for Madison Square Garden and Chase Center",
       x = "Word", y = "Frequency")

For this graph, I am comparing the ten most common words used when reviewing Madison Square Garden and the Chase Center. For the Chase Center it was all words you are expecting like seats, game, and food. For Madison Square Garden one of the most common words was “people”. This makes sense because of how packed it is. Money was also a common theme among the reviews as well.

Conclusion

Based off my results, I learned that an experience at Madison Square Garden is a lot different than the experience at the Chase Center. Every stadium in the NBA is so unique and there are different pros and cons that come with each stadium. That is why it is so cool to visit a new stadium to watch a basketball game.