Brews

BREWS REVIEWS

This is a quick little rundown of some observations made based on the sentimental value of words used in a data set of not one by two bererys, Madtree and Rhinegeist.

Top Ten Most used words in these brewerys reviews

This is a quick little rundown of some observations made based on the sentimental value of words used in a data set of not one by two bererys, Madtree and Rhinegeist.

tidy_brews %>%
  group_by(word) %>% 
  summarize(n = n()) %>% 
  arrange(-n)

## # A tibble: 5,697 × 2
##    word           n
##    <chr>      <int>
##  1 beer        1018
##  2 brewery      414
##  3 space        363
##  4 beers        334
##  5 bar          313
##  6 pizza        296
##  7 love         291
##  8 food         266
##  9 time         259
## 10 rhinegeist   255
## # … with 5,687 more rows

Positivity of the reviews

The following graphic shows that majority of reviews are positive.

brews_counts %>%
  filter(n > 40) %>%
  mutate(n = ifelse(sentiment == "negative", -n, n)) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(word, n, fill = sentiment)) +
  geom_col() +
  coord_flip() +
  labs(y = "Contribution to sentiment")

Reviews Question

Does the individual brewery reviews compare to one one another in setemtal reviews?

##Splits the data by the brewerys and displays
 
  brews_sentiment %>% 
  ggplot(aes(x=sentiment, y = `Percent of scoreable words`, fill=brewery))+
         geom_col(position = "dodge") +
  scale_y_continuous(labels = scales::percent) +
  labs(title = "A comparison of the emotive sentiments found in the Madtree and Rhinegeist reviews",
       caption = "Both berwerys have very similare emotive sentiment in their reviews",
       x = "Sentiment",
       fill = "Brewery")