Data Analysis Assignment
Introduction
This document is an analysis of the mcdonalds_reviews dataset, the gamestop_product_reviews dataset, and the comments of YouTube video picked by me
Initial Set-Up
In order to conduct this analysis, we first have to add libraries to our file to give us the functions needed to carry out the analysis
Question 1
Part A
Part B
Part C
Part C, (i)
| Word | Sentiment | Count |
|---|---|---|
| anger | bad | 185 |
| anticipation | time | 522 |
| disgust | bad | 185 |
| fear | bad | 185 |
| joy | food | 866 |
| negative | bad | 185 |
| positive | food | 866 |
| sadness | bad | 185 |
| surprise | good | 278 |
| trust | food | 866 |
Part C, (ii)
Part D
| Bigram | Count |
|---|---|
| fast food | 153 |
| customer service | 116 |
| ice cream | 61 |
| worst mcdonalds | 52 |
| 10 minutes | 49 |
| parking lot | 43 |
| worst mcdonald’s | 42 |
| 15 minutes | 39 |
| chicken nuggets | 38 |
| french fries | 34 |
| mickey d’s | 33 |
| 20 minutes | 32 |
| 5 minutes | 29 |
| iced coffee | 29 |
| dollar menu | 28 |
| late night | 28 |
| sweet tea | 27 |
| 24 hours | 25 |
| chicken sandwich | 23 |
| quarter pounder | 23 |
Part E
| Trigram | Count |
|---|---|
| ice cream machine | 10 |
| worst customer service | 10 |
| 24 hour drive | 9 |
| eat fast food | 8 |
| fast food restaurants | 8 |
| ice cream cone | 8 |
| 10 piece chicken | 7 |
| fast food restaurant | 7 |
| sausage egg mcmuffin | 7 |
| terrible customer service | 7 |
| free wi fi | 6 |
| ice cream cones | 6 |
| piece chicken nugget | 5 |
| piece chicken nuggets | 5 |
| worst fast food | 5 |
| 2 apple pies | 4 |
| 5 10 minutes | 4 |
| bad customer service | 4 |
| double cheese burger | 4 |
| fast food chain | 4 |
| fast food joint | 4 |
| fast food joints | 4 |
| sausage egg biscuit | 4 |
| spicy mcchicken sandwich | 4 |
| wait 10 minutes | 4 |
| waited 15 minutes | 4 |
Part F
Part (i)
Most of the reviews containing the word ‘Waiting’ were bad. They all mentioned long waiting times, instances of rude staff, and instances of customers being given wrong orders
Part (ii)
All of the reviews containing the words ‘Shamrock Shake’ were also bad. They all mentioned the unnatural green colour of the shake, as well as the taste being bad and unnatural
Part (iii)
All of the reviews containing the words ‘Ice Cream Machine’ were also bad. They all mention staff telling the customers that the ice cream machine isnt working, but it never seems to be working.
Part G
Positive Word Cloud
Negative Word Cloud
Question 2
A LDA_Gibbs topic model with 15 topics.
Question 3
Top 20 Words
Most Common Words Per Sentiment
# A tibble: 20 × 3
# Groups: sentiment [2]
sentiment word n
<chr> <chr> <int>
1 negative worst 215
2 negative bad 185
3 negative wrong 179
4 negative slow 137
5 negative rude 120
6 negative cold 113
7 negative horrible 81
8 negative dirty 71
9 negative hard 66
10 negative problem 65
11 positive like 500
12 positive good 278
13 positive right 239
14 positive fast 232
15 positive work 188
16 positive pretty 146
17 positive well 141
18 positive hot 132
19 positive nice 132
20 positive better 130
Most Common Bigrams
| Bigram | Count |
|---|---|
| NA NA | 293 |
| gordon ramsay | 228 |
| rip coolio | 215 |
| coolio joke | 209 |
| hot sauce | 196 |
| 22 00 | 180 |
| gordon ramsey | 160 |
| haven’t heard | 143 |
| didn’t age | 99 |
| pepto bismol | 89 |
| coolio comment | 85 |
| love gordon | 84 |
| scrambled eggs | 71 |
| coolio line | 69 |
| 22 02 | 65 |
| da bomb | 62 |
| havent heard | 53 |
| r.i.p coolio | 41 |
| hot wings | 40 |
| lime juice | 40 |
Word Cloud
Topic Modelling
A LDA_Gibbs topic model with 15 topics.