NBA Stadium Sentiment Analysis

Background

I will be using data for the arenas of the two best players identified from NBA stuffer. The two best players are Joel Embiid (Philadelphia) and Luca Doncic (Dallas). Philadelphia plays at the Wells Fargo Center and Dallas plays at the American Airlines Center. I will be using Trip Advisor to conduct this research and answer the questions.

Question #1

Are the customers of the Wells Fargo Center more satisfied or the American Airlines Center customers?

Data #1

I intend to collect review data from Trip Advisor for both stadiums to evaluate the sentiment to see what customers are saying about both arenas. This data will provide evidence of which arena is preferred by seeing which is more positive. I will be using the NRC Lexicon.

Visualization #1

By using this bar chart you can see that Wells Fargo Arena is perceived as more negative than American Airlines Arena because there are a lot more negative words associated with the reviews.

`summarise()` has grouped output by 'sentiment'. You can override using the
`.groups` argument.

Question #2

Are the reviews of the Wells Fargo Center or the American Airlines Center more positive?

Data #2

I intend to collect review data from Trip Advisor for both stadiums to evaluate the sentiment to see what customers are saying about both arenas. This data will provide evidence of which arena is preferred by seeing which is more positive. Using the Bing Lexicon.

Visualization #2

This visualization shows that only a few words that are used more than once have been used between the two arenas. American Airlines Arena only has two unique negative words, however, one is the word concession, which is most likely referring to a concession stand and does not have a negative connotation. Wells Fargo Arena has four unique negative words. American Airlines Arena has 11 positive words used total and 3 negative words that count. Wells Fargo Arena has 19 total positive words and 8 negative words. This means that despite having more negative words, the Wells Fargo Arena’s reviews are more positive.

`summarise()` has grouped output by 'arena'. You can override using the
`.groups` argument.
Joining with `by = join_by(word)`
`summarise()` has grouped output by 'arena'. You can override using the
`.groups` argument.

# A tibble: 4 × 3
# Groups:   arena [2]
  arena                   sentiment     n
  <chr>                   <chr>     <int>
1 Wells Fargo Arena       positive     30
2 Wells Fargo Arena       negative     23
3 American Airlines Arena negative     20
4 American Airlines Arena positive     20

Question #3

In what parts of the reviews of the Wells Fargo Center or the American Airlines Center are the reviews more positive or negative and which one is more positive or negative?

Data #3

I intend to collect review data from Trip Advisor for both stadiums to evaluate the sentiment to see what customers are saying about both arenas. This data will provide evidence of which arena is preferred by seeing which is more positive. Using the Bing Lexicon and Chronological Comparison of Valence.

Visualization #3

The visualization shows that the reviews usually start out positive and quickly turn negative just to repeat the process over again. It appears that the Wells Fargo Center has more positive reviews overall.

Joining with `by = join_by(word)`
`summarise()` has grouped output by 'arena', 'index'. You can override using
the `.groups` argument.