Visualizing social media content with SentenTree · 2017-01-01 · Visualizing social media content...
Transcript of Visualizing social media content with SentenTree · 2017-01-01 · Visualizing social media content...
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Visualizing social media content with
SentenTree
Mengdie Hu , Krist Wongsuphasawat , and John Stasko
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• Keywords
• Text cloud, Wordle, semantic word cloud, parallel tag cloud, etc.
• Text structures
• Word Tree, Double Tree, etc.
• PhraseNet
Visualizing textual content
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Wordle, Viegas et al. 09
Seam-carving word cloud, Wu et al. 11
Parallel tag cloud, Collins et al. 09
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Word tree, Wattenberg and Viegas 08
Phrase net, Van Ham et al. 09
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http://loveallthis.tumblr.com/post/166124704
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http://loveallthis.tumblr.com/post/166124704
Recurring segments
collapsed and larger
Non-recurring segments
aligned
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• Highlight topics with keywords
• Show keywords in context
• Coverage and conciseness
• Show fragments instead of full sentences
• Remove stopwords and punctuations
• Collapse common fragments
• Summary and Details
• Show original context on demand
• Enable drill-down
Design goals of SentenTree
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SentenTree visualization demo
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Building a SentenTree vis
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• I love the World Cup!
• RT if you are watching the World Cup today!
• Marcelo opens the World Cup scoring with this own goal to give
Croatia the Lead.
• The first goal of the World Cup is an own goal by Brazil!
• This is the best goal so far in the World Cup: Marcelo’s own goal.
Constructing frequent sequential patterns
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• I love the World Cup!
• RT if you are watching the World Cup today!
• Marcelo opens the World Cup scoring with this own goal to give
Croatia the Lead.
• The first goal of the World Cup is an own goal by Brazil!
• This is the best goal so far in the World Cup: Marcelo’s own goal.
Constructing frequent sequential patterns
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• world cup (5)
• world cup own goal (3)
• goal world cup own goal (2)
Constructing frequent sequential patterns
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• world cup (5)
• world cup own goal (3)
• goal world cup own goal (2)
Constructing frequent sequential patterns
goal – world cup – own goal
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• Implemented with the CoLa package
• http://marv1.infotech.manash.edu/webcola/
• Constraints:
• Words in a pattern: left to right order
• Bigrams: shorten edge, center words vertically
• Child branches: center horizontally
Force-directed layout with constraints
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• Time cost
• Pattern generation: linear to number of sentences in dataset
• Graph layout: depends on complexity of graph
• Javascript implementation, Chrome browser, Macbook Air
• Test results:
• 10k – 2 sec
• 100k – 20 sec
• 1M – 2 minutes
Performance
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Observations and initial feedback
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Natural content clustering
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• Like
• Frequent words “pop out” with frequency count
• Context available through hovering
• Similar content grouped together
• Shows major topics
• Less frequent content discoverable
Data analysts feedback
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• Learn
• Same word appears multiple times
• Drill down, search, etc.
• Suggest
• Improve packing algorithm to remove whitespace
• Reduce noise
• Stemming and spell checks
• Filters
Show more information: meta-info on user, sentiment, etc.
Data analysts feedback
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• SentenTree is a novel visualization technique for social media text
that both summarizes topics (through keywords) and shows context
(through sentence fragments).
Takeaway
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This research is supported in part by
the DARPA XDATA program and the
National Science Foundation via award
IIS-1320537.
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• SentenTree is a novel visualization technique for social media text
that both summarizes topics (through keywords) and shows context
(through sentence fragments).
SentenTree: Make TextVis great again