merupakan tugas projek akhir dari Digitalent Scholarship

1. DIGITAL TALENT
SCHOLARSHIP
2019
# P A P U A
Kelompok 3Kelas B
• Muhammad Andhika
• Muhajir Akbar Hsb
• Muammar Fazli

2. INTRODUCTION
# P A P U A
Why #PAPUA ?
D I G I TA L TA L E N T S C H O L A R S H I P
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3. ROADMAP
# P A P U A
Crawling
Marketers must link
the price to the real.
Modelling
Marketers must link
the price to the real.
Result
Marketers must link
the price to the real.
Prepocessing
Marketers must link
the price to the real.
Visualization
Marketers must link
the price to the real.
D I G I TA L TA L E N T S C H O L A R S H I P
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4. DATA COLLECTING
# P A P U A
LIBRARY
Python Library for
collecting data from
Twitter
HASHTAG
Search Query
Tweepy #PAPUA EN RUN
LANGUAGE
Get only english
language tweet
CRAWL
Start Crawling Data
D I G I TA L TA L E N T S C H O L A R S H I P
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* Code running in Amazon EC2 Service

5. DATA PREPOCESSING
# P A P U A
Cleaning
• Clean unused
attributes
Remove # and @
• Using `re` python library
Remove URL
• Using `re` python library
Correcting Word
• Correcting Shortword
(Replaced with dict)
Remove Stopword
• Using nltk.corpus
python Library
Rawdata Output
D I G I TA L TA L E N T S C H O L A R S H I P
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6. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
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MODELLING
1 2 3
Latent Dirichlet Allocation
Social Network Analysis
Modelling
Clustering
# P A P U A
3
4
Sentiment

7. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
2 0 1 9 SENTIMENT
# P A P U A
TEXTBLOB
• Positive Content
• Negative Content
• Netral Content

8. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
2 0 1 9 CLUSTERING
# P A P U A
hierarchical clustering
(Ward D)
Euclidean Distance Sparse = 0.95

9. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
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# P A P U A
Plot Type : Stars Sparse = 0.95
SOCIAL NETWORK
ANALYSIS

10. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
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LATENT DIRICHLET
ALLOCATION
# P A P U A
8 Topics 4 Terms

11. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
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VISUALIZATION
# P A P U A

12. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
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# P A P U A
TERM FREQUENCY

13. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
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# P A P U A
TERM FREQUENCY – POSITIVE DATA

14. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
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# P A P U A
TERM FREQUENCY – NEGATIVE DATA

15. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
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# P A P U A
TERM FREQUENCY – NETRAL DATA

16. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
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# P A P U A
CLUSTERING

17. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
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# P A P U A
CLUSTERING – POSITIVE DATA

18. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
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# P A P U A
CLUSTERING – NEGATIVE DATA

19. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
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# P A P U A
CLUSTERING – NETRAL DATA

20. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
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# P A P U A
SOCIAL NETWORK ANALYSIS

21. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
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# P A P U A
SOCIAL NETWORK ANALYSIS – POSITIVE DATA

22. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
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# P A P U A
SOCIAL NETWORK ANALYSIS – NEGATIVE DATA

23. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
2 0 1 9
# P A P U A
SOCIAL NETWORK ANALYSIS – NETRAL DATA

24. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
2 0 1 9
# P A P U A
LATENT DIRICHLET ALLOCATION

25. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
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# P A P U A
LATENT DIRICHLET ALLOCATION – POSITIVE DATA

26. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
2 0 1 9
# P A P U A
LATENT DIRICHLET ALLOCATION – NEGATIVE DATA

27. Rawdata
D I G I TA L TA L E N T S C H O L A R S H I P
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# P A P U A
LATENT DIRICHLET ALLOCATION – NETRAL DATA

28. VISUALIZATION
# P A P U AD I G I TA L TA L E N T S C H O L A R S H I P
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29. VISUALIZATION
# P A P U AD I G I TA L TA L E N T S C H O L A R S H I P
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