EA Sports is known for making popular sports games like Madden, Fifa, and NHL. Over the years, people are starting to turn on EA because they are getting lazy with the amount of work they have put into their games are now starting to lack detail because there is not competitor when it comes to a football or soccer game. The question I am looking to answer is, are the reviews for EA games the same for Madden and Fifa even though they have different production teams and a different consumer interest?
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library(tidyverse)
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library(ggraph)
I scraped a variety of different reviews from positive and negative. There is 100 reviews for each game.
New names:
Rows: 100 Columns: 3
── Column specification
──────────────────────────────────────────────────────── Delimiter: "," chr
(1): reviews.review dbl (1): ...1 lgl (1): reviews.steam_purchase
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Rows: 100 Columns: 3
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(1): reviews.review dbl (1): ...1 lgl (1): reviews.steam_purchase
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Tidying the Data
Next I want to Tidy the data so that I could figure out key words that people are using so that you can figure out if they are saying positive or negative things about the games.
# A tibble: 676 × 2
word n
<chr> <int>
1 game 65
2 football 15
3 gameplay 15
4 play 14
5 shit 14
6 fifa 12
7 time 10
8 ea 9
9 feels 8
10 fun 8
# ℹ 666 more rows
tidy_EAFC %>%pairwise_count(item = word, feature = reviews.steam_purchase, upper =FALSE) %>%arrange(-n)
# A tibble: 228,150 × 3
item1 item2 n
<chr> <chr> <dbl>
1 fc 25 2
2 game rush 1
3 game 5 1
4 rush 5 1
5 game mode 1
6 rush mode 1
7 5 mode 1
8 game fun 1
9 rush fun 1
10 5 fun 1
# ℹ 228,140 more rows
# A tibble: 736 × 2
word n
<chr> <int>
1 game 95
2 madden 36
3 play 34
4 ea 18
5 pc 14
6 bad 13
7 buy 13
8 football 13
9 time 13
10 fun 12
# ℹ 726 more rows
tidy_Madden %>%pairwise_count(item = word, feature = reviews.steam_purchase, upper =FALSE) %>%arrange(-n)
# A tibble: 263,390 × 3
item1 item2 n
<chr> <chr> <dbl>
1 game madden 2
2 game win 2
3 madden win 2
4 game plays 2
5 madden plays 2
6 win plays 2
7 game offense 2
8 madden offense 2
9 win offense 2
10 plays offense 2
# ℹ 263,380 more rows
For this first visualization, I wanted to show the positive and negative words combined and see if there is a trend of positive or negative words at the top. For Madden, There seems to be a lot more negative but a lot of positive near the middle.
Madden_counts %>%group_by(sentiment) %>%slice_max(n, n =10) %>%ungroup() %>%ggplot(aes(x =reorder(word, n), y = n, fill = sentiment)) +geom_col() +coord_flip() +labs(title ="Most Common Positive and Negative Words in Madden Reviews",x ="Word",y ="Count" ) +scale_fill_manual(values =c("positive"="steelblue", "negative"="firebrick")) +theme_minimal()
This next visualization, I wanted the words to pop out at you and these are the words that could sway someone to buying the game or not. When first looking at this, I see a lot of red and a lot of words that you don’t want to see when you are figuring out if you want to buy a game or not.
This last visualization I wanted to show was to show the most frequent words in reviews. What this really shows is that there are more frequent negative words than positive which again is something that you don’t want to see when buying a game.
Madden_counts %>%group_by(sentiment) %>%slice_max(n, n =10) %>%ungroup() %>%ggplot(aes(x =reorder(word, n), y = n, fill = sentiment)) +geom_col() +facet_wrap(~ sentiment, scales ="free_y") +coord_flip() +labs(title ="Top Words Contributing to Positive and Negative Sentiment",x ="Word",y ="Frequency" ) +theme_minimal()
EAFC Visualizations
I decided to make the same 3 visualizations but for EAFC 26. These reviews were a lot worse and it looks like the EAFC fan base is a lot more passionate about their game than Madden. You can tell their fans want change bad.
EAFC_counts %>%group_by(sentiment) %>%slice_max(n, n =10) %>%ungroup() %>%ggplot(aes(x =reorder(word, n), y = n, fill = sentiment)) +geom_col() +coord_flip() +labs(title ="Most Common Positive and Negative Words in Madden Reviews",x ="Word",y ="Count" ) +scale_fill_manual(values =c("positive"="steelblue", "negative"="firebrick")) +theme_minimal()
EAFC_counts %>%group_by(sentiment) %>%slice_max(n, n =10) %>%ungroup() %>%ggplot(aes(x =reorder(word, n), y = n, fill = sentiment)) +geom_col() +facet_wrap(~ sentiment, scales ="free_y") +coord_flip() +labs(title ="Top Words Contributing to Positive and Negative Sentiment",x ="Word",y ="Frequency" ) +theme_minimal()
Conclusion
In conclusion, EA is doing a terrible job a producing games and putting out a product that is scamming their fan base. These reviews show that there needs to be a competitor in the market that would allow them to focus on their game and put more details into it.
