Bachelor's thesis presentation

1. Twitter data mining: social media as a
rising customer service channel

2. Statistics
500 million tweets per day
67% US customers use social media for customer
service (J.D. Power and Associates, 2013)
33% prefer to contact brands using social media rather
than the telephone (Nielsen, 2012)

3. Social customer service
My mac book is slowly dying on me, its been
an hour and its still like this!! @AppleSupport
#helpme #macissues #ahhh
@alrollitt We'll be glad to look into this with
you. DM us with any troubleshooting steps
that have been taken, and we'll get started.
@AppleSupport, @SamsungSupport, @LGUSA-Mobile,
@Moto_Support, @HTCUSA

4. Data collection
Twitter API + Python = Tweets

5. 32, 312 Tweets
20.4 - 4.5

6. Data analysis
Clean it up!

7. Data analysis
Tweets —> Conversations

8. Customer service sessions
Figure 1 Length of interaction
0
1500
3000
4500
6000
No response (1) Short integration (2-4) Long interaction (<5)
34%
55%
11%
Number of total tweets in each interaction

9. Why no response???
34%
55%
…
…
If not including those messages, response rate is close to 100%

10. My mac book is slowly dying on me, its been
an hour and its still like this!! @AppleSupport
#helpme #macissues #ahhh
@alrollitt We'll be glad to look into this with
you. DM us with any troubleshooting steps
that have been taken, and we'll get started.

11. Response time
34%
55%
11%
Figure 2 Response time distribution

12. Customer analytics
34%
11%
Figure 3 Follower count of Twitter customers
Table 1 Most influential customers

13. Text analysis
Natural language processing + Machine learning = Insights
Text preprocessing
Sentiment analysis
Text mining

14. Text preprocessing:
Tweets have complicated linguistic features
—> Tokenize: “#”, “RT”, “:)”,”http://bit.ly/23BJwMB”
—> Stemming: “u” = “you” = “ya”
—> Stopword removal: “is”, “,” ,”would”
Tweets

15. Text mining
What the tweet is about?

16. Sentiment analysis
Is a tweet positive or negative?
Naive Bayes Classifier + Supervised machine learning =
smart computer

17. Sentiment analysis
Is a tweet positive or negative?
Naive Bayes Classifier + Supervised machine learning =
smart computer

18. Sentiment analysis
“Computer, learn this pls” “Now tell me the sentiment of those”
10,000 Training tweets 2,000 Testing tweets

19. Sentiment analysis
Figure 4 Sentiment score of interactions with various length

20. Conclusions
• Market leader’s Twitter customer service - responsive &
effective
• Twitter customers - more active and influential
• Customers expect fast problem resolution, although long
service effort is appreciated too
• Twitter is a rich information source - performance
monitoring, market insights, customer opinion & feedback

21. Questions & Answers