N. Novielli, F. Calefato, F. Lanubile. “Towards Discovering the Role of Emotions in Stack Overflow” – In Proceedings of the 6th International Workshop on Social Software Engineering pp. 33-36, ACM 2014 ************************************************************************************************************ Today, people increasingly try to solve domain-specific problems through interaction on online Question and Answer (Q&A) sites, such as Stack Overflow. The growing success of the Stack Overflow community largely depends on the will of their members to answer others’ questions. Recent research has shown that the factors that push members of online communities encompass both social and technical aspects. Yet, we argue that also the emotional style of a technical question does influence the probability of promptly obtaining a satisfying answer. In this presentation, we describe the design of an empirical study aimed to investigate the role of affective lexicon on the questions posted in Stack Overflow.

1. Towards Discovering
the Role of Emotions
in Stack Overflow
N. Novielli, F. Calefato, F. Lanubile
University of Bari, Italy
{nicole.novielli, fabio.calefato, filippo.lanubile}@uniba.it

2. A new way to access knowledge
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3. How Do Programmers Ask
and Answers Questions?
 Which questions are answered well and
which ones remain unanswered?
(Treude et al., ICSE’11), (Asudazzaman et al., MSR’13)
 Can we predict how long a question will remain
unanswered? (Asudazzaman et al., MSR’13)
 What are the main discussion topics?
(Barua et al., ’12), (Bajaji et al., MSR’14)
 What are the main factors affecting reputation?
(Bosu et al., MSR’13)

4. Emotions in Social
Computing and SSE
 Sentiment Analysis on Yahoo! Answers
(Kucuktunc et al., WSDM’12)
 Answers perceived as good have a more neutral
sentiment than others
 Do developers feel emotions? (Murgia, et al., MSR’14)
 Apache Software Foundation issue tracker
 Sentiment Analysis of Commit comments in
GitHub (Guzman et al., MSR’13)
 Correlation with day and time, programming language,
team distribution
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5. Research Question
Getting emotional while asking or
answering questions in Stack Overflow:
good or bad?
 Impact on success of questions
 Impact on perceived quality of answers
 Correlation with reputation
 Correlation with topics
 …
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6. Preliminary study
 RQ1:To what degree does the emotional
style of a question affect the probability of
success?
 A successful question has an accepted answer
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8. Dataset distribution
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No
accepted
Answers
(31%)
No Answers
(11%)
Accepted
Answers
(58%)
Successful
4,196,125
questions
Unsuccessful
3,013,677 questions

9. Building the Model
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Post Properties
• Title Length
• Post Length
• Code Blocks
• Day
• Time
• Topic
• # Comments
Social Factors
• Question Score
• Answer Score
• # Accepted answer
provided
• # Answers accepted
• # Badges
Affective Factors
•Sentiment Polarity
• Polarity of Question/Answer
• Polarity of Comments
•Lexical Cues of Affective
States
• Positive emotions lexicon
• Negative emotions lexicon
• Gratitude
• Politeness
• Attitude of doubt
• …
Control Model

10. The Model
Post Properties Social Factors Affective Factors
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Control Model
Independent variables, logistic regression model
Dependent variable: success of a question (Y/N)

11. Post Properties - Metrics
• Title and Post Length: # words
• Alhoff at al., @ICWSM’14; Asaduzzaman et al., @MSR’13
• Used by SO moderators for automatic filtering
• Code Blocks: yes/no
• Treude et al., @ICSE’11
• Day: in {weekday, weekend}
• Bosu et al., @MSR2013
• Time: in {morning, afternoon, evening night}
• Bosu et al., @MSR2013
• Topic: categorical, using LDA
• Asaduzzaman et al., @MSR’13; Bosu et al., @MSR’13
• Harper et al., @CHI’08
• Barua et al., Empirical Software Engineering 2014
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12. Social Factors - Metrics
• Assessing the reputation of the author of the
question at the time it is posted
• High status correlated with success in Reddit.com (Althoff et al., ICWSM’14)
• Novices’ questions are more likely answered on Stack Overflow
(Treude et al., ICSE’ 11)
• Metrics to approximate the author’s
reputation
• Question Score: upvotes - downvotes on questions
• Answer Score: upvotes – downvotes on answers
• # Accepted answer provided
• # Answers accepted
• # Badges: total badges owned
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13. Affective Factors
• Sentiment Polarity
• Questions/Answers
• Polarity of Comments
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14. Sentiment Analysis Emotion Detection
Subjective vs. Objective
Negative vs. Positive
Classification using Discrete
Emotion Labels Goal
‘I can't solve this problem, it’s very
frustrating’
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Example
Resources - SentiStrength
(Thelwall et al., 2012)
- SentiWordNet
(Esuli and Sebastiani, 2006)
- MPQA Lexicon
(Wilson et al., EMNLP’05)
- …
- LIWC
(Tausczik and Pennebaker, 2010)
- WordNet Affect
(Strapparava and Valitutti, 2004)
- Depeche Mood
(Staiano and Guerini, ACL’14)
- …
Sad, Frustrated
‘I can't solve this problem, it’s very
frustrating’
Subjective, Negative

15. Affective Factors
• Sentiment Polarity
• Question
• Polarity of Comments
• Lexical Cues of Affective States
• Positive emotions lexicon
• Negative emotions lexicon
• Gratitude
• Politeness
• Attitude of doubt
• …
Future work
- Sentistrength: http://sentistrength.wlv.ac.uk/
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16. SentiStrength
 Estimates the strength of both positive and
negative sentiment in questions and comments
 Robust also for informal language
 Used in previous research
 Sentiment Analysis of commit comments in GitHub
(Guzman et al., MSR’13)
 Sentiment Analysis on Yahoo! Answers
(Kucuktnc et al., WSDM’12)
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17. Preliminary results - Post Properties
17
Coeff Odds Ratio
Code Blocks 0.2549 1.29
# of comments -0.3659 0.69
Day (Weekend) 0.0131 1.01
TIME
Afternoon 0.1418 1.15
Evening 0.2093 1.23
Night 0.1085 1.12
Post LENGTH
Body Length -0,0004 0.99
Title Length -0.0039 0.99
All significant, with a = 0.05
• Review questions are more
concrete and get more answers
(Treude et al., ICSE’11) and vague
questions remain unanswered
(Asaduzzaman et al., MSR’13)
• SO off-peak hours (night): longer
answer interval and less
questions posted
(Barua et al., MSR’13)

18. Post properties: Topic
18
Coeff Odds Ratio
DATABASES/PERFORMANCE 0.4062 1.50
WEB PROGRAMMING 0.2725 1.31
GRAPHICS 0.2415 1.27
WEB PROGRAMMING/HTTP 0.1441 1.16
JAVA 0.0029 1.00
OOP 0.8599 2.36
MOBILE DEVELOPMENT/iOS 0.2664 1.30
SOURCE CODE MANAGEMENT 0.2805 1.32
DATA STRUCTURE/ALGORITHMS 0.7340 2.08
.NET FRAMEWORK/ASP 0.3442 1.41
SCRIPTING 0.3649 1.44
DATABASES/SQL 0.4488 1.57
WEB APP DEVELOPMENT 0.3330 1.40
MOBILE DEV/ANDROID 0.1111 1.12
All significant, with a = 0.05

19. Success rate per topic
19
Topic Success rate
Number of
questions Post rate
OOP 6 70,81% 630258 8,84%
DATA STRUCTURE/ALGORITHMS 9 67,73% 798713 11,20%
DATABASES/SQL 12 61,12% 582130 8,16%
.NET FRAMEWORK/ASP 10 58,73% 518834 7,28%
SCRIPTING 11 58,54% 497763 6,98%
WEB APP DEVELOPMENT 13 58,47% 492173 6,90%
DATABASES/PERFORMANCE 0 57,72% 415825 5,83%
WEB PROGRAMMING 1 56,59% 536255 7,52%
SOURCE CODE MANAGEMENT 8 55,37% 373397 5,24%
GRAPHICS 2 54,37% 383376 5,38%
MOBILE DEVELOPMENT/iOS 7 53,91% 376517 5,28%
WEB PROGRAMMING/HTTP 3 52,22% 375510 5,27%
MOBILE DEV/ANDROID 14 51,50% 432095 6,06%
JAVA 5 49,35% 235489 3,30%
WEB AUTHENTICATION/API 4 49,00% 482992 6,77%

20. Preliminary Results –
Social Factors
Coeff Odds Ratio
User Question Score* -0,0017 0.99
User Answer Score* -0,0002 0.99
User Answers Accepted* 0,0047 1.00
User Questions Accepted* 0,0078 1.00
Number Of Badges 0,0001 1.0001103
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*significant with a = 0.05

21. Preliminary Results –
Affective Factors
Coef Odds Ratio
SENTIMENT of the QUESTION
Question Positive Score -0.0248 0.98
Question Negative Score -0.0083 0.99
SENTIMENT of the author’s COMMENTS
Comment Positive Score -0.1813 0.83
Comment Negative Score -0.1080 0.90
All significant, with a = 0.05
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22. Impact of Positive Sentiment on Success
Positive polarity of QUESTION Positive polarity of COMMENTS
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23. Impact of Negative Sentiment on Success
Negative polarity of QUESTION Negative Polarity of COMMENTS
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24. Problems in detecting
sentiment
 ‘Problem’ lexicon is too peculiar for the domain
to be considered as a pure expression of
negative emotions
 Actually describing emotions
 ‘I have very simple and stupid trouble […] I'm pretty
confused, explain please, what is wrong?’ (neg=-2)
 ‘Sorry for troubling you guys’ (neg=-2)
 Simply describing problem
 What is the best way to kill a critical process? (neg=-2)
 What is wrong? (neg=-2)
 Mixed
 I’m missing a parenthesis . But where? :( (neg=-3)
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25. - Thanks! Preliminary
qualitative analysis using
LIWC
- Positive score = 3
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26. Next steps
 Separate positive emotions from gratitude
expressions
 Qualitative analysis using of the first 1000
questions with highest positive sentiment score
 Gratitude and politeness are the most frequent cases
 ‘Cheers’, ‘Thanks (in advance)’, ‘Thank you’, …
 Gratitude is positively associated to success of
requests (Althoff et al., 2014)
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27. Next steps
 Further lexical analysis
 Assessing the suitability of state-of-the-art tools for
sentiment analysis
 Modeling the ‘success lexicon’
 Classification study: is success predictable?
 Preliminary results: 0.67 accuracy
 Investigate other research questions
 Emotions and perceived quality of answers
 Emotions and reputation
 Emotions and topics
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29. Thank you
N. Novielli, F. Calefato, F. Lanubile
University of Bari, Italy
{nicole.novielli, fabio.calefato, filippo.lanubile}@uniba.it