See conference video - http://www.lucidimagination.com/devzone/events/conferences/ApacheLuceneEurocon2011 One of the main features of Solr is Faceted Search. Facets are the top terms present in the results of a query. But facets do not indicate the most statistically relevant terms of a query, that is, these terms that are more present in the documents selected by the query than in the rest of the collection. A critical factor in making such statistical insights broadly useful is to make them visual -- i.e., using charts and graphs that display these quantitative relationships. We will present how to adapt Ajax-Solr to find the most prominent terms of a query compared to the full set or just another query. We are going to present and example on how this can be used to find current topics in the news, and extract that information into visually communicative charts and graphs.

1. Adapting Ajax Solr to Compare
different sets of documents
Joan Codina-Filba, Barcelona Media-Innovation Center
19-Oct-2011

2. What I Will Cover
 More than just Facets
 We do research on opinion mining
 To Give an overview of thousands of
opinions
 After analyzing the contents of them:
• How to explore the key concepts expressed
in the opinions.
• Are the key concepts the most common
ones, or the most differentiating?
 Use of Solr and Ajax Solr for a more
intelligent data navigation
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3. My Background
 Joan Codina Filbà
 Barcelona Media – Innovation Center
 Senior Researcher
 Experience on
• Data => Text => Web => Opinion MINING
• Integration of NLP tools.
• Multimodal search
• Some teaching
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4. The Challenge
 Companies want to know what does the web 2.0
say about them:
 Too many writers, in many places, not enough time
to read.
– Detect hot topics (Quality, Price, service?)
– Detect strengths / weakness
– Knowledge about the market
 Use NLP to detect key concepts in text
 User generated content, noisy, irony, dispersion
 From data to information and knowledge
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5. Use Case Example
 Analyze the users in MySpace
 Combination of demographic data and
analyzed text
 Possibility to "navigate" the data to get
“knowledge” of the users.
 Exploring thousands of messages, before
sending them to a “blind” , “automatic” text
mining tool to find relevant aspects.
 An “OLAP” for data and text, a kind cube view
of the opinions
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6. Solr Facets
 Simple statistic...
– For fields with a single value:
SELECT field, count(x) FROM table GROUP BY field
• Simple aggregation statistic
– For text fields ?
 Facets vs IDF
– The most common facets give less
information
– The rare ones, are not useful
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7. Information given by Facets
8 1
Facets
0,9
7 idf
information 0,8
6
0,7
5 0,6
4 0,5
0,4
3
0,3
2
0,2
1 0,1
0 0
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
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8. Information given by Facets
8 1
Facets
0,9
7 idf
information 0,8
6
0,7
5 0,6
4 Too Too 0,5
rare Common 0,4
3
0,3
2
0,2
1 0,1
0 0
0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1
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9. The Information is in the
“not so common" terms
 Doing a search and looking at facets to
characterize the data:
– For most of the queries the most common
facets do not change significantly
– Sorting by frequency:
• The order almost is the same.
• Difficult to see the changes
 The information is not on the term's frequency
but in the relative changes on these frequencies
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10. Example
 Exploring MySpace posts
 Demographic Data: Age, Gender, Country
 Text Content
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11. Example
 After a query (country = MX)
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12. Example
 After a query (country = MX)
Find the 10 differences !!
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13. Example
 After a query (gender= Male)
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14. Example
 After a query (gender= Male)
Find the 10 differences !!
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15. The Information is on the
differences
 What do we want to know:
– Which are the terms that use the Males and/or
people from Mexico ?
– OR
– Which are the terms that better identify or
differentiate them?
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16. Example
 After a query (country = MX)
Substract them !!
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17. Example
 After a query (country = MX)
Faceted Results: The most used words by the mexicans
Statistical Results: The words that differentiate the mexicans
from the rest of the world
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18. Example
 After a query (gender = M)
Faceted Results: The words that Males use most
Statistical Results: The words that differentiate the males from
the females
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19. The information is on the
difference
 We can compare a subset with the full set:
 But we can compare different subsets:
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20. The information is on the
difference
 We can compare by gender, doing different
queries
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21. Data Charts
 We can apply differences to charts
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22. Data Charts
 We can apply differences to charts: And show the
difference with the global set
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23. Data Charts
 Data Charts can show the differences
Males
Females
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24. Data Charts
 Even through queries
Males
Females
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25. Ajax Solr
 Single Query / Resultset
Query
Ajax
Interface SOLR
Resultset
User Browser
Javascript front end Server
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26. Ajax Solr Extension
 Multiple Queries / Resultsets
Query
Ajax
Interface SOLR
Resultset
User Browser
Javascript front end Server
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27. Ajax Solr Extension:
Multiple Sets
 Each set has its own query
– A query is composed of different query terms
– The terms that define the set, can not be
deleted
– Query terms can be copied/deleted through
the different sets
 Widgets can view/compare data from the
different result sets.
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28. Ajax Solr Extension:
Multiple Sets
Set 0
Independent widgets
Base Data
Set 1 Set 2
Widgets related to this set Widgets related to this set
Own Filter Query Own Filter Query
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29. Conclusions
 Facets give basic statistics
 The information is on the differences
 Ajax Solr can be adapted to manage different
sets of data
 Charts and graphics help us to have an
overview of the data: information.
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30. Sources
The code is not ready to be published
• Was done for SolrJs
• Migrating to AjaxSolr (Lucene-Solr 4.0)
But it will be...
 Links
– http://www.barcelonamedia.org/personal/joan.co
dina/en
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