A sentiment analysis of brand perception of twitter following a failure event of Uber Ice-Cream in Vancouver

1. Brand
Perception
Analysis: UBER
ARAV MANAKTALA
DM18207

2. About UBER
UBER Technologies Inc. is an American private hire company headquartered in San Francisco,
California, United States, operating in 633 cities worldwide. It develops, markets and operates the
UBER car transportation and food delivery mobile apps. UBER drivers use their own cars although
drivers can rent a car to drive with UBER..
UBER Stunt Marketing
UBER uses multiple marketing strategies. One of them is Stunt Marketing. In this the company
engages in publicity stunts. A publicity stunt is “an event staged to get public attention or for
marketing purposes”. These campaigns have helped UBER gain a huge customer base, while word
of mouth remains the . Examples of two such campaigns are
UBER Ice-Cream
For just one day each summer, UBER
delivers ice cream on demand in cities
around the world.
01
UBER Puppies
UBER brings animals(puppies) from
shelters to play with for 15 mins. The
service is free and most organizations
part of the initiative are open to
donations
02
UBER

3. UBER Ice-Cream
UBER as part of its marketing has been running UBER Ice-Cream campaigns across cities.
Vancouver was supposed to have this campaign on 25th, August 2017. The campaign had social
background with UBER donating up to $3000 to a children's charity.
Earnest Ice-Cream
The campaign was also being organized with Earnest Ice Creams who were providing the ice-cream
about to be circulated. The aim was to support local business. Though in between the campaign
Earnest withdrew its association with UBER due to difference in values
UBER Vancouver
Vancouver has not approved of ride hailing apps like UBER, hence the success of this campaign was
of utmost importance for UBER to build Brand Equity in the market.
The actual campaign had opposite results though.
UBER Ice-Cream

4. The campaign was available in the marked region on 25th August 2017 between 11am – 3pm.
How UBER Ice-Cream work

5. After UBER announced its UBER Ice-Cream campaign in Vancouver.
#UBERIcreamFail was trending on twitter on 25th of August. There seemed to be some issue with deliveries of the
The data was extracted from twitter using ‘twittR’ package in R and hashtags : “UBERIceCream” & “UBERIceCreamFail”.
About the Data
Collected between
25/08/17 –
12/09/17
1.5k Tweets
Collected
Hashtags Used
#UBERIceCream
#UBERIceCream
Fail

6. Data Cleaning
Data cleaning was done using SnowballC package, by first creating a corpus a data type used in for
text mining techniques. The data is first converted in lowercase then the following operations are
applied to the Cropus.
Remove Stop words
Remove stop words using English
dictionary and other words which can
hinder the analysis.
02
Remove Whitespaces
Remove any extra whitespaces in the
data, that might exist before or after the
string
05
Data Preparation
Remove Punctuations
Remove things like comma, and other
special characters from the data
01
Remove Single letter
Words
Remove single letter words, as they do
not convey any meaning
04
Remove URL
Remove website links, as they do not
convey any meaning in this context
03
Stem the Corpus
Convert the words in their root form and
then correct the misspelt words due to
stemming
06

7. After a quick look at the frequency we can ‘fail’ seems to be the most commonly used word.
Frequency
Many people used the hashtag #UBERIceCreamFail.
Moreover, this points towards UBER’s failure to deliver a
promise and the people responding with anger.
This refers to the type of Ice-Cream about to be given out
Fail
Sandwich
Right
The right has a different story. It relates to Women's rights
march which happened in January in Vancouver (the
location of campaign). Moreover UBER had recently been
under the scanner for sexual harassment case.

8. Frequency
app
Here the people were referring to the UBER
application and how it was not working
work
Work is again a common word. The context of this
word is explored further
thing
This was in the context in term of ‘things’ people in
Vancouver care about

9. UBER
The word correlation is high with “Bryan Loewen” an influencer and “women”. Even though the people were angry about not getting ice-creams. The reactions are highlights other malpractices of
UBER. This shows the consumer behaviour for revenge where a mistake at one end can blow the whole scenario out of proportion.
Correlation

10. Correlation
Fail
This retweet is in the context of getting ice-cream from different source i.e. an influencer “Van Coffee Snob”, an anonymous café reviewer in Vancouver. This account started a context after UBER
Ice Cream was unable to fulfill its promise and failed. Hence this shows most people claiming failure of UBER to deliver also participated in this draw of Ice-Creams.

11. Associations
Using the findAssoc() function associations of a few words was found.
• Fail is being associated with contest being run by ‘Van Coffee Snob’
• UBER has been associated words foreshadow, meaning warning also the name is being spoiled
using ‘goober’, hence it is another word to look for during future brand analysis
• Sandwich is the reference to Ice-Cream Sandwich which was offered as part of campaign. Most
associations are towards the campaign being run by
• App was in context of UBER’s mobile application. People seem to talk about how the campaign
fail
random retweet ya pick instead vancoffeesnob back
0.71 0.71 0.71 0.70 0.69 0.69 0.68
buy
0.68
uber
foreshadow oper handl machin propaganda violat goober execut hype
0.41 0.41 0.41 0.41 0.41 0.41 0.41 0.29 0.29
harass anyth face worker mind watch
0.29 0.23 0.23 0.23 0.20 0.20
sandwich
random retweet ya pick vancoffeesnob buy instead
0.87 0.87 0.87 0.85 0.85 0.84 0.84
back
0.83
app
heathermichiko one backfir delete realli button
0.38 0.36 0.36 0.35 0.31 0.26
get sneakygood work
0.25 0.23 0.22

12. Associations
• UBER Fail was a hashtag being used after the campaign was unable to deliver its promise to the
customer
• Women was used in context women's rights (Vancouver March) and the other issues currently
going on with UBER, specifically the sexual harassment culture. Due to which the CEO had
resigned
• Rights was also used in context of Women's rights. Also ‘Bryan Loewn’ a local Vancouver twitter
user seems to influence considerable people on the topic
$uberfail
bridg outrag yoga eaten grate hoseacheung starv build
0.34 0.34 0.34 0.28 0.28 0.28 0.28 0.28
confid goodwil save hasnt
0.28 0.28 0.27 0.26
$women
two bryanloewen either doesnt love thing
0.97 0.97 0.97 0.90 0.88 0.76
$right
two bryanloewen either doesnt love thing now yveehop
0.74 0.74 0.74 0.69 0.67 0.57 0.40 0.29
bed shit
0.26 0.21

13. Topic Modelling
Topic modelling is a technique to find underling abstract topics being discussed
in the text. 5 Topics with 7 term were identified.
• Topic 1: Seems to talk about the UBER Ice Cream campaign and how
people responded to it. Some thanked and other claimed the failure. This
topic gets more discussion traction long after the campaign is over and
people are still reacting
• Topic 2: Talks about the application and how it stopped working. During the
campaign.
• Topic 3: This topic is about the raffle being run by “Van Coffee Snob”. Lost
traction towards the end of the campaign.
• Topic 4: This topic about the women's rights. It seems it spiked just after the
incident and started to trickle down over the days.
• Topic 5: This is the side promo being run by Earnest Ice-Cream, who were
partnering with UBER for the campaign.
Topics
Topic 1 : "ubericecreamfail, im, refresh, still, hour, pr, thank"
Topic 2 : "free, today, try, app, availible, work, just"
Topic 3 : "fail, sandwich, back, buy, instead, pick, vancoffeesnob"
Topic 4 : "right, love, thing, doesnt, two, either, bryanloewen"
Topic 5 : "earnesticecream, thank, people, scream, think, skipthedish, cant"
Topic Modelling

14. Sentiment Analysis
the process of computationally identifying and categorizing opinions expressed
in a piece of text, especially in order to determine the attitude of a writer towards
a particular topic, product etc.
How it was done?
• The sentiment analysis here is done using sentiment library from GitHub.
• The package was used to divide all text in positive or negative emotions
across dates and displayed using a streamgraph.
What it means?
• Most of the reactions in the aftermath of the campaign are negative which is
expected as the campaign didn’t go as planned.
• Moreover, other incidents and news surrounding UBER seems to be of
negative nature. Hence, higher negativity is expected.
• There is also a drop in activity around this hashtag around 5th September.
This might signify the relevance of the topic was dropping as less people
were discussing it.
• The overall perception of the UBER brand seems to be negative even though
the campaign was for a good cause, the failure to deliver dwells on UBER.
Sentiment Analysis
Positive Negative

15. Word Emotions Analysis
Similar to Sentiment Analysis, word emotion analysis divides the text into
different emotions.
How it is done?
A package named “syuzhet” is used for this analysis. “syuzhet” uses NRC
Emotion lexicon. The NRC emotion lexicon is a list of words and their
associations with eight emotions (anger, fear, anticipation, trust, surprise,
sadness, joy, and disgust) and two sentiments (negative and positive).
The get_nrc_sentiment function returns a data frame in which each row
represents a sentence from the original file. The columns include one for each
emotion type
What it means?
• Anticipation is expected to be high as many people were looking forward to
the free ice-creams across Vancouver.
• UBER was able to create the buzz around the campaign before hand, hence
awareness within the target population was high, hence anticipation was high
• High anger can be explained as the anticipation was high but, UBER failed to
deliver. This has angered many of its users
• Trust being high signifies the text is talking about trust a lot. But, the context
of the text seems to be of loss of trust in UBER by people of Vancouver.
Word Emotion Analysis

16. Conclusion
UBER’s Brand Perception
UBER seems to have negative Brand Perception. Even though they have seen exponential growth it has not come without making enemies in forms of
other taxi groups or government responses.
Way Forward
• UBER should ensure that it’s PR events do not fail as people seem to drag the name of the company in all the negative contexts.
• UBER’s Ice-Cream campaign has seen good traction with people anticipating ordering ice creams and having positive brand perception. This can
allow it to get an edge over its competitors like Lyft in the region.

17. Conclusion(contd.)
Way Forward
• The sexual harassment scandal at Uber has significantly damaged the companies image. UBER should disentangle themselves from this image with
help of campaigns and physical evidence to support it.
• The campaign in Vancouver looks like a emotional rollercoaster with all kinds of emotions being displayed by the people. But if anger and lack of trust
stick for long UBER will lose its ground in the region completely. UBER needs to fix things in Vancouver and they should respond to it fast.
• Shortly after the promotion ended Vancouver based Earnest Ice Cream distanced itself from the promotion calling it a "mistake.“ This damaged
UBER’s image further. UBER should also focus on better PR partners as in this case they are the face of the promotion and their brand image got
damaged

18. LIMITATIONS
• The text extracted is form one source and of one type only.
• The Brand perception is post a failed PR stunt and in midst of corporate crisis, hence the
perception is bound to be negative. A time bound study will give better insights on Brand
Perception
• The tweets are taken across 2 weeks due to logistic and twitter API constraints.
• A clear dependency on how the packages perceive the text is established, hence different
packages might lead to different results.
• The context of conversation in Twitter was diverted due to the failure and other influencers. Hence
the data might have its flaws which are not visible.
Limitations of the Study

19. THANK
YOU