Comparison of Aer Lingus and United Airlines Reviews
Introduction
My family only flys Aer Lingus Airlines when we fly internationally. I wonder, are we missing out on other airlines? So, this report explores the sentiment and emotions through reviews on Aer Lingus and United Airlines. This data was scraped and collected from the Skytrax website, a place for airline reviews.
Questions:
Do positive and negative sentiments differ amongst Aer Lingus and United reviews?
Does one airline have more negative emotions within the reviews than the other?
Are there more positive/negative comments in certain months of the year? Or has there been a trend over the years?
Load Data from Previous Web scraping
Load Packages
Load Data files
Clean up and Combine Data files into one
Start Reviewing Words Within Reviews
First, I looked at related words for each airline:
Joining with `by = join_by(word)`
Joining with `by = join_by(word)`
`summarise()` has grouped output by 'airline'. You can override using the
`.groups` argument.
`summarise()` has grouped output by 'airline'. You can override using the
`.groups` argument.
# A tibble: 1,682 × 3
# Groups: airline [1]
airline word n
<chr> <chr> <int>
1 United flight 220
2 United united 126
3 United verified 70
4 United told 53
5 United time 52
6 United hours 51
7 United trip 51
8 United airline 47
9 United service 41
10 United airport 39
# ℹ 1,672 more rows
Warning: The `trans` argument of `continuous_scale()` is deprecated as of ggplot2 3.5.0.
ℹ Please use the `transform` argument instead.
The words are the same, which makes sense because words like “flight” and “trip” are used when talking about any airline or flight.
I decided to go deeper and first find the sentiment scores for each airline and then find the negative and positive words within each airlines reviews.
Sentiment Scores using NRC Lexicon:
# Load in the NRC lexicon# Make sure to follow the prompts in the console!nrc <-get_sentiments("nrc")# Visualize emotional sentiment counts for each breweryairlines %>%inner_join(nrc, by ="word", relationship ="many-to-many") %>%group_by(sentiment, airline) %>%summarize(n =n()) %>%ggplot(aes(x = sentiment, y = n, fill = airline)) +geom_bar(stat ="identity", position ="dodge") +scale_fill_manual(values =c("lightgreen","darkblue")) +labs(title ="Airline Sentiment Scores",subtitle ="Total number of emotive words scored ",y ="Total Number of Words",x ="Emotional Sentiment",fill ="Airline")
`summarise()` has grouped output by 'sentiment'. You can override using the
`.groups` argument.
There is not much to compare with this visual. However, I feel like this sentiment visual answers question number 1 as no. There is not much positive or negative difference in sentiment scores for these two airlines. I think we should dive deeper by seeing which words are used more frequently for each airline.
Positive and Negative Words:
`summarise()` has grouped output by 'airline'. You can override using the
`.groups` argument.
Joining with `by = join_by(word)`
`summarise()` has grouped output by 'airline'. You can override using the
`.groups` argument.
# A tibble: 4 × 3
# Groups: airline [2]
airline sentiment n
<chr> <chr> <int>
1 Aer Lingus negative 194
2 United negative 178
3 Aer Lingus positive 106
4 United positive 87
I chose to include words used 5 or more times because I scraped 70 reviews for each airline. The fact that “delayed” was included in 37 of these 70 reviews for United Airlines is interesting, and making me think my family’s choice in airline is good and we might not be switching over to United anytime soon.
Here, we can answer questions one and two…
Do positive and negative sentiments differ amongst Aer Lingus and United reviews? Yes, as you can see, there is more variation and negative words used in United Airline reviews than Aer Lingus reviews. I found it interesting that the word “delayed” was used 31 more times in United reviews than Aer Lingus reviews.
Does one airline have more negative emotions within the reviews than the other? Yes, I would say that United reviews have more negative emotions within the reviews than Aer Lingus. Let’s look into this idea a little more…
Positive and Negative Word Scores:
Here, I found positive and negative scores for each airline based on the words used in reviews on their airlines.
I also found this by month of the year because the reviews are recorded by what month it was posted in, and I wanted to answer question three.
Are there more positive/negative comments in certain months of the year? Or has there been a trend over the years?
Joining with `by = join_by(word)`
`summarise()` has grouped output by 'airline', 'month'. You can override using
the `.groups` argument.
Joining with `by = join_by(word)`
`summarise()` has grouped output by 'airline', 'month'. You can override using
the `.groups` argument.
These visuals are inverses of each other, but as we can see, there are significantly more negative words and emotions used within these airline reviews.
United airlines does not have one month of the year where there is a higher positive score than negative score.
We can see that in the summer months, July, August, and September, there are a lot more reviews for United, and these are all more negative than positive. I think this is due to high travel period. I think both airlines see more negative comments and emotions exemplified because most people complain about airlines. There are not that many people that applaud or compliment airlines. As humans, we just want to safely and comfortably get to our destination so our vacation or trip can begin. Also, many people are already crabby when they board their flight to come home from their vacation or trip, simply because it is over. They had a great time and they do not want it to end. So anything going a little bit wrong or off during their flight experience can put a bad taste in their mouth and cause them to write a negative review on the airline. Also, if a flight is delayed, people are forced to sit in an airport with nothing to do. So many have the extra time to sit down and complain about an airline. If a flight goes well, people will just go on with their life happily, but not express that happiness in a review.
