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Why People Hate Your App
Making Sense of User Feedback in a
Mobile App Store
Bin Fu, Jialiu Lin, Lei Li,
Jason Hong, Christos Faloutsos, Norman Sadeh
Carnegie Mellon University, University of California Berkeley
KDD’13 Industry Track
2
32.4GB data:
180k Android app
until Nov 2012.
13M user reviews
......
awesome
good but
needs
improving
crash, pls
fix!!!
What are users complaining about?
8/15/2013 Lei Li @ KDD 2013 3
awesome
good but
needs
improving
crash, pls
fix!!!
What are market trends?
8/15/2013 Lei Li @ KDD 2013 4
All build upon a single question
8/15/2013 Lei Li @ KDD 2013 5
How to
summarize
millions of
comments
with ratings?
Beyond histogram
WisCom to rescue
• S1: Per review analysis
–Inconsistent reviews
• S2: Per app analysis
–Root causes
–Dynamic view
• S3: Whole market analysis
–Trends and insights
8/15/2013 Lei Li @ KDD 2013 6
Unstable
Costly Unattractive
S2: Meso Analysis:
Dynamic View of Root Causes
7
• Summarization of all comments / app
• Root causes over time
8/15/2013 Lei Li @ KDD 2013
S2: Dynamic View: Life Story of App
8Negative reviews Positive reviews
“What’s in it?” J.Kleinberg
0
10
20
30
40
50
60
70
80
90
100
day
#ofcomments
…
0
10
20
30
40
50
60
70
80
90
100
day
#ofcomments
S2: Dynamic View: Life Story of App
9
stability cost
Stability Cost
Connectivity Compatibility
0
10
20
30
40
50
60
70
80
90
100
day
#ofcomments
S2: Dynamic View: Life Story of App
10
12/30/2011 fix
it! It keeps force
closing on stage
1, need an
update, please!!!
stability cost
Stability Cost
Connectivity Compatibility
0
10
20
30
40
50
60
70
80
90
100
day
#ofcomments
S2: Dynamic View: Life Story of App
11
stability cost
Stability Cost
Connectivity Compatibility
“What’s in it?”
0
10
20
30
40
50
60
70
80
90
100
day
#ofcomments
S2: Dynamic View: Life Story of App
12
stability cost
Stability Cost
Connectivity Compatibility
stability cost
connectivity
0
10
20
30
40
50
60
70
80
90
100
day
#ofcomments
S2: Dynamic View: Life Story of App
13
stability cost
Stability Cost
Connectivity Compatibility
stability cost
connectivity
05/30/2012 Would
give 0 stars if I
could. Server error.
0
10
20
30
40
50
60
70
80
90
100
day
#ofcomments
S2: Dynamic View: Life Story of App
14
stability cost
Stability Cost
Connectivity Compatibility
stability cost
connectivity
05/30/2012 Would
give 0 stars if I
could. Server error.
06/06/2012 Finally
fixed. Hooray >:)
S2: Dynamic View: Life Story of App
15
Stability Cost
Connectivity Compatibility
#ofcomments
day
S3: Macro Analysis:
Discovery of Market Trends
16
• User prefs for different types of apps?
8/15/2013 Lei Li @ KDD 2013
S3:Outstanding prefs in market categories
• Games, same top complaints
• Non-games: different top complaints
10%
18%
55%
0% 10% 20% 30% 40% 50% 60%
cost
stability
attractiveness
31%
56%
0% 10% 20% 30% 40% 50% 60%
connectivity
accuracy
weather
business
Users’ Reaction to Free and Paid Apps
18
100 most reviewed applications and games
equally
scattered
complaints
Unstable
Costly Unattractive
Free
apps
G: Game
A: other apps
Paid apps
??
Unstable
Costly Unattractiv
Paid
apps8/15/2013 Lei Li @ KDD 2013
Users’ Reaction to Free and Paid Apps
19
100 most reviewed applications and games
equally
scattered
complaints
Unstable
Costly Unattractive
Free
apps
G: Game
A: other apps
Unstable
Costly UnattractivPaid
apps8/15/2013 Lei Li @ KDD 2013
Users more tolerant to cost
of games than applications
Conclusions
• WisCom discovers
–Inconsistency
–Root causes
–Market trends
• leili@cs.berkeley.edu
• Stay tuned for online
version
208/15/2013 Lei Li @ KDD 2013
Unstable
Costly Unattractive

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Why People Hate Your App: Making Sense of User Feedback

Notas do Editor

  1. Distinct Features of App ReviewsShorter in length More typos, slangs, not structured sentence Reviews of the same app can target to different versions
  2. Detect inconsistent comments/ratings;Identify root causes of users’ negative reviewsTrack the evolving patterns of user reviewsDiscover market trends
  3. Observation 1: spikes corresponds to versions of apps
  4. Arcade & Action Brain & Puzzle Cards & Casino RacingSports Games CasualBusiness: e-wallet, square
  5. 100 most reviewed free applications and 100 most reviewed free games, paid and paidApplication received more unified complains. Left…. , game can attribute to multiple reasons Horizontally, 1 higher quality in generalUsers more tolerant to cost of games than applications
  6. 100 most reviewed free applications and 100 most reviewed free games, paid and paidApplication received more unified complains. Left…. , game can attribute to multiple reasons Horizontally, 1 higher quality in generalUsers more tolerant to cost of games than applications
  7. Sentiment analysis, extract the vocabulary that can be used for further analysisDoes this review make sense?Sample 8% of the commentsRemove HTML tags and delete non-English reviewsSegment comments into wordsRemove rarely used words.- Yield 19K distinct words, from 988K comments.
  8. Before we go that direction, there is another small application we want to talk about Might attributed to careless mistakes or intentional manipulationInconsistent reviews were removed for later analysis
  9. Objective function of the regression model
  10. However, most of the words only express strong feeling, but not telling us the reasons. Let’s see another set of words that we picked up from the model
  11. This is also motivate us to proceed to the next level, to find the root cause of bad reviews
  12. Q1: What are the advantages and defects for a particular app?Q2: What are main complaints from users? What are main complaints from my competitors? Automatic summarizationQ3: Is there any anomalies or spam comments? Is there any difference among different categories of apps? What are the market trends?
  13. Some contains features, like , picture, media, telephon, some relate to performance such as stability, compativility, etc. Others talking about cost. These 10 topics are the most significant ones that detected cross the whole market