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!"#$%&!'%!!"#$!!()*!'+&"!,)*!(*#-!%&#'!./&!01!2#*3!"4&!!05!678(&)! ![anSary nE kha myry ray^E myN eamr shyl ayk bd dmaG awr Zdy XKS hyN ] !

[Ansari said, “according to me Aamir Sohail is one crazy and stubborn man”]!TARGET

Attributes ID:t1

AGENT Attributes ID:a1 Nested-source: “w” TargetID:t1

!""#"$%&'!(()*+,(-.!"#$%&'&(!)**+*,-./01.$!2.3%*+4.!"2*.25+*0$!6+36(!!7.3%*+4.8*9:%;-$!*&!<95+*+4.8*9:%;-$!2,==!

EXPRESSIVE ELEMENT Attributes ID:ex1 , TargetID:t1, Emotion:anger Intensity:high, Nested-Source: “w”, a1, Polarity:negative

Non-Topical Analysis

Agent: Opinion holder Target: Target of Opinion being expressed (a topic, a person, organization etc.) Attitude: includes Expressive Element

www.janyainc.com

FACETED SEARCH: DRILL DOWN TO RELEVANT CONTENT/DATA

People are filled with anger and sorrow because of the policies made by Musharaf. OPINION HOLDER – Writer, People

TARGET –Musharaf’s policies (Musharaf is an implied target)

Human Behavior Analysis •  Process social media content, provide tools for analysts to:

•  Identify social networks: groups, members •  Identify topics of discussion and sentiment

•  E.g. angry at govt., wanting retaliation, peacemakers •  Thought influencers

•  Identify social goals through analysis of verbal communication

•  Manipulation: Persuasion, threats, coercion •  Religious supremacy: religious analogues •  recruitment

Social Media Content

Link Diagrams

Predictive Modeling

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Name translation output:

l66/,&"3<0%7,0-6%"0$/S&%3&>"UF"UF"D&S0%3&2™"3<0%7,0-6%"6G"%0S&7C"

mi6H&@&<'"j>$90-6%"+.%.73&<"+6:0SS0>"i0%&&G"N3S0<"<&9&%3,F".%"NG/:0%.730%'"k"S.,,.6%"9:.,><&%"6G"79:66,"3&23U66J7":0@&"U&&%">.73<.U$3&>"36"76S&":6P&Cn"

?0-@&",0%/$0/&"P<69&77.%/"<&`$.<&>"G6<"_%&]/<0.%&>"0%0,F7.7o"

Google Translation Context Aware Translation

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166,7"S0>&"G6<"+DN"G0.,"6%"N<0U.9">.0,&937"

Q("

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Code Mixing, Switching

!  Use of Latin script: lack of transliteration standards makes it difficult to process

!  Urdish, Spanglish, Hinglish etc.

Afsoos key baat hai . kal tak jo batain Non Muslim bhi kartay hoay dartay thay abhi this man has brought it out in the open. [It is sad to see that those words that even a non muslim would fear to utter until yesterday, this man has brought it out in the open]

Solutions: •  Apply “romanized” POS tagger, English tagger in tandem: use machine learning to combine evidence and generate final tag, language ID •  For longer English spans, use English NLP system

Language Resource Acquisition

Less Commonly taught languages (LCTL) •  Yoruba, Russian, Swahili •  Dialects

Very few few linguistics resources available •  electronic lexicons •  translation lexicons •  part-of-speech taggers, chunkers

• Typically, very expensive to produce these resources by hand

•  The web provides a new opportunity to automatically acquire these resources “web as corpus”

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