Omer Yuksel, Eindhoven University of Technology

1. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
We Have Our Ways: Extracting and Analyzing
Online Confessions
¨Omer Y¨uksel1
o.yuksel@tue.nl
1Eindhoven University of Technology
14.06.2016
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

2. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Introduction
Web mining
Application of traditional data mining techniques to Web data
Data sources
Usage
Structure
Content
Motivation
Profit
Insight
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

3. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Ek¸si S¨ozl¨uk (‘Sour Dictionary’)
Collaborative dictionary/forum in Turkish
Similarities to Urban Dictionary and Reddit
Around since 1999
Inspired by Hitchiker’s Guide to the Galaxy
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

4. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Ek¸si ˙Itiraf (‘Sour Confessions’)
Specific thread in Eksi Sozluk
Started out as a parody
Turned into an actual confessions dump
Motivation
Relatively untouched
One of the largest and interesting threads in the website
Extra challenges of the Turkish language
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

5. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Turkish language
https://upload.wikimedia.org/wikipedia/commons/b/b3/Turkic_languages%2C_location_map.png
Language family: ‘Turkic’
Loanwords from Arabic, Persian, and French
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

6. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Turkish language
https://upload.wikimedia.org/wikipedia/commons/b/b3/Turkic_languages%2C_location_map.png
Extended Latin script (ı,¨o,¨u,¸c,¸s,ˇg,˙I)
Too many homonyms
Agglutinative, i.e. extensive use of suffixes
C¸ekoslovakyalıla¸stıramadıklarımızdanmı¸ssınızcasına: ‘as if you were
one of those whom we could not turn into Czechoslovakian’
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

7. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Tasks
Data collection (Scrapy, BeautifulSoup)
Preprocessing (Snowballstemmer)
Exploration (Pandas, Matplotlib, Seaborn)
Machine learning (Scikit-learn)
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

8. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Concepts
Users (authors) post entries in a thread.
All entries are timestamped.
Popularity: Entries can be up- or downvoted, or bookmarked
(favourites).
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

9. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Web crawling
Scrapy with XPath queries.
Beautiful soup to convert HTML text into plaintext.
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

10. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Scrapy Output
Output in JSON:
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

11. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Tokenization
Break text into a sequence of words
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

12. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Stopwords
Common function words such as ‘and’, ‘at’, ‘for’ in English
Generally not helpful for machine learning tasks
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

13. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Stemming
Getting rid of suffixes:
C¸ekoslovakyalıla¸stıramadıklarımızdanmı¸ssınızcasına
#Unique words: 347153 → 147658
https://pypi.python.org/pypi/snowballstemmer
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

14. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Statistics
Total Entries 92520
Authors 16094
Words 5736867
Characters 40392500
Unique Words 147658
Characters 132
Median Entries per author 2
Characters per entry 211
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

15. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Words distribution
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

16. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Words distribution
Nouns Thing, life, work, time, place, dictionary, moment,
friend, home, girl
Adjectives one/single, beautiful/nice, happy, real, difficult
Verbs be, come, want, mean, happen, say, see, stop, look
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

17. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Favorites distribution
26% not favorited.
56% favorited once.
1% favorited more than 10 times.
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

18. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Visibility vs Favorites
By default, only the current day’s entries are shown
Entries sorted by time of the current day.
Entries posted at midnight have the highest visibility.
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

19. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Machine Learning
Steps:
Vectorization
Dimensionality reduction
Training
Evaluation
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

20. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Vectorization
Bag-of-words:
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

21. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Latent Dirichlet Allocation
Document as a mixture of topics
Topics as a distribution of words
Inferring topics from data: Gibb’s sampling
Parameter: #topics
Output: topic vector
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

22. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Latent Dirichlet Allocation
Interesting topics (most frequent words):
heart, tissue, stomach, tooth
song, hair, music, youtube, listening
photo, message, load, facebook, account
car, money, rich, media
dictionary, entry, confession, author, thread, sour
you, me, phone, hate, lover
Also seemingly nonsensical ones:
cat, mirror, water, (to) feel
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

23. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Predicting #Favorites
Input data: Topic vector representing an entry text
Extra challenge: count data (integer), time-dependence
Regression vs classification?
Split entries into three classes:
Class 0 1 2
#Favorites 0 1 − 4 > 5
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

24. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Evaluation methodology
Split the data into training (80%) and test (20%) sets.
No temporal overlap.
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

25. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Results
Accuracy:
Cross validation Test
Random forests 62% 52%
Na¨ıve Bayes (Multinomial) 63% 46%
Majority missclassified as class 1 (somewhat popular)
Text alone not enough to predict popularity
Dependence on external variables
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

26. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Results
If only used daytime and text length as features:
Cross validation Test
Random forests 55% 50%
Na¨ıve Bayes (Gaussian) 73% 52%
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

27. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Summary
Web → structured data → exploration → prediction
‘Quality’ of the text does not guarantee popularity.
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

28. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Future work
Character n-grams
Learning important words/topics
Tracking conversations
Chatbots
Other datasets
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions

29. Introduction
Data collection
Preprocessing
Exploration
Machine Learning
Conclusions
Thank you
Contact: o.yuksel@tue.nl
https://github.com/omersyuksel/eksi
¨Omer Y¨uksel PyData—Extracting and Analyzing Online Confessions