This project was completed during the Lviv Data Science Summer School 2017 (http://cs.ucu.edu.ua/en/summerschool).

1. P02. ODS Slack exploration

2. OpenDataScience Slack
Officially started 12.03.2015
● Started as platform for open data science communication
● The biggest data science community in the world
As for today 25.07.2017
● 1.3M messages, 5400 users (2000 weekly active)
Most active channels:
#deep_learning, #theory_and_practice, #visualization, #_general,
#_meetings, #_jobs, #big_data, #python, #r, #datasets, #nlp, #edu_courses

3. Our data:
● Users
● Messages and Threads
● Time response
● Reactions
● Replies

4. Graphs

5. Channels 2016
Node size - PageRank
Clustering - walktrap
Data - messages in top-40
channels

6. Nodes size - Page Rank
Clustering - walktrap
Channels 2017
Node size - PageRank
Clustering - walktrap
Data - messages in top-40
channels

7. Most active users
in all threads
Node size - weighted degree
Clustering - modularity
Data - threads in all channels

8. Most active
users in
random_flood
channel
Node size - weighted degree
Clustering - modularity
Data - threads in
_random_flood

9. Most active
users in career
channel
Node size - weighted degree
Clustering - modularity
Data - threads in career
channel

10. Plots

19. Analysis of users’ activity

20. Detection of curious users
● Curious user - user who asks for help
● NLP techniques: preprocessing, regular expressions
Expert detection
● Experts - users with the highest numbers of specific reactions under his/her
messages in threads
Troll detection
● Trolls - users with the highest numbers of specific reactions under his/her
messages
Model Info

28. Response Time Prediction

29. Problem Info
Problem:
Predict time of response in thread
Data:
12500 threads, time period 2016 - 2017
Tool:
Regression models

30. Data Stats
Features:
● Text of main message
● Day and hour of main message
● Length of main message
● Channel
● Mentioned users
● Links in text
● Historical activity
Target variable:
● Waiting time for response

31. Applied Approaches
Approaches:
● Lasso regression (Scikit learn)
MAE = 149 min
● XGBoost regression
MAE = 140 min
● Lightgbm regression
MAE = 119 min
Best results: Lightgbm
Plot for real and predicted response time
(in minutes) for deep_lerning channel:

32. Further Work
Future improvements:
● New features (for example, use number of active users in channel and
number of threads before new thread)
● Use answers in channel also
● Reduce dimensionality
● Take into account the topic of thread

33. Users Classification

34. Users Classification
Problem:
Classify users by messages
Data:
users - 1771 (select top 100)
12 channels, time period 2016 - 2017
messages - 120 997

35. User Classification Tools
Regression models - accuracy 19.85%
● LogisticRegression - 19.85% (CountVectorizer, OneVsRestClassifier)
● LinearSVC - 15.63%
LSTM - accuracy 16.56%

36. Channels Classification

37. Channels Classification
Problem : Classify channels by messages
Data : messages - 120 997 time period 2016 - 2017
12 channels #career
#big_data
#kaggle_crackers
#lang_python
#lang_r#theory_and_practice
#nlp
#welcome
#bayesian
#_meetings
#datasets
#deep_learning

38. Channel Classification Tools
Preprocessing - pymorphy2 lemmatization, exclude: stop_words/url/emoji
Regression models - accuracy 55.33%
● LogisticRegression - 55.33% (CountVectorizer, OneVsRestClassifier)
● LinearSVC - 51.52%
CNN (with fasttext embeddings) - accuracy 51.67%
LSTM (with fasttext embeddings) - accuracy 55.42%
Ensemble - accuracy 58.17%

39. Channels Classification eli5

40. Channels Classification eli5

41. Channels Classification eli5

42. Our Team
Volodymyr Medentsiy
Vadym Korshunov
Ganna Kaplun
Andrii Skliar
Yana Mosiichuk
Kateryna Bobrovnyk
Vitalii Radchenko

44. Thanks!
https://github.com/udsclub/ucu_se
ntiment/tree/master/projects/p02