A Noun Phrase Analysis Tool for Mining Online Community Conversations Caroline Haythornthwaite <haythorn@uiuc.edu> Anatoliy Gruzd <agruzd2@uiuc.edu>

Problem Online communities are creating a growing volume of texts B y 2010, > 70% of all digital information will be user-generated . ( T echnology C onsultancy IDC ) And the majority of it will still be text-based How can we analyze and make sense of such a vast amount of textual data? Possible Solution : Automated text analysis

Online communities are creating a growing volume of texts

B y 2010, > 70% of all digital information will be user-generated . ( T echnology C onsultancy IDC )

And the majority of it will still be text-based

How can we analyze and make sense of such a vast amount of textual data?

Possible Solution :

Automated text analysis

Research Questions Can we automate the process of creating an “ effective representation” of the texts produced by online communities? A “ representation” is an excerpt [of the original text] that describes or may stand in for the original text. An “ e ffective ” representation can help us to answer: Can we discover what the community interests and priorities are? Can we discover patterns of language and interaction that characterize a community?

Can we automate the process of creating an “ effective representation” of the texts produced by online communities?

A “ representation” is an excerpt [of the original text] that describes or may stand in for the original text.

An “ e ffective ” representation can help us to answer:

Can we discover what the community interests and priorities are?

Can we discover patterns of language and interaction that characterize a community?

Why Nouns & Noun Phrases? Nouns and noun phrases tend to be the most informative elements of any sentence Noun phrases make it easy to disambiguate the meaning of words e.g. ‘travel information ’, ‘ information center’, ‘ information management’ Noun phrases extraction is easy and cheap to accomplish with today’s Natural Language Processing (NLP) tenchiques NLP is a set of computational techniques for processing natural (human) languages

Nouns and noun phrases tend to be the most informative elements of any sentence

Noun phrases make it easy to disambiguate the meaning of words

e.g. ‘travel information ’, ‘ information center’, ‘ information management’

Noun phrases extraction is easy and cheap to accomplish with today’s Natural Language Processing (NLP) tenchiques

NLP is a set of computational techniques for processing natural (human) languages

Example of Noun-Phrase Extraction “ We will have minivan during the conference to help shuttle attendees from the other hotels to and from the Kellogg Center. ” Step 1. Part-of-Speech Tagging <We / PRP > <will / MD > <have / VB > <minivan / NN > <during / IN > <the / DT > <conference / NN > <to / TO > <help / VB > <shuttle / VB > <attendees / NNS > <from / IN > <the / DT > <other / JJ > <hotels / NNS > <to / TO > <and / CC > <from / IN > <the / DT > <Kellogg / NNP > <Center / NNP > Step 2. Chunking <We / PRP > <will / MD > <have / VB > <minivan / NN > <during / IN > <the / DT > <conference / NN > <to / TO > <help / VB > <shuttle / VB > <attendees / NNS > <from / IN > <the / DT > ( NP : <other / JJ > <hotels / NNS > ) <to / TO > <and / CC > <from / IN > <the / DT > ( NP : <Kellogg / NNP > <Center / NNP > ) Representation <We / PRP > <will / MD > <have / VB > ( NP : < minivan / NN > ) <during / IN > ( NP : <the / DT > < conference / NN > ) <to / TO > <help / VB > <shuttle / VB > ( NP : < attendees / NNS > ) <from / IN > ( NP : <the / DT > < other _ hotels / NNS > ) <to / TO > <and / CC > <from / IN > ( NP : <the / DT > < Kellogg _ Center / NNP > )

“ We will have minivan during the conference to help shuttle attendees from the other hotels to and from the Kellogg Center. ”

Step 1. Part-of-Speech Tagging

<We / PRP > <will / MD > <have / VB > <minivan / NN > <during / IN > <the / DT > <conference / NN > <to / TO > <help / VB > <shuttle / VB > <attendees / NNS > <from / IN > <the / DT > <other / JJ > <hotels / NNS > <to / TO > <and / CC > <from / IN > <the / DT > <Kellogg / NNP > <Center / NNP >

Step 2. Chunking

<We / PRP > <will / MD > <have / VB > <minivan / NN > <during / IN > <the / DT > <conference / NN > <to / TO > <help / VB > <shuttle / VB > <attendees / NNS > <from / IN > <the / DT > ( NP : <other / JJ > <hotels / NNS > ) <to / TO > <and / CC > <from / IN > <the / DT > ( NP : <Kellogg / NNP > <Center / NNP > )

Representation

<We / PRP > <will / MD > <have / VB > ( NP : < minivan / NN > ) <during / IN > ( NP : <the / DT > < conference / NN > ) <to / TO > <help / VB > <shuttle / VB > ( NP : < attendees / NNS > ) <from / IN > ( NP : <the / DT > < other _ hotels / NNS > ) <to / TO > <and / CC > <from / IN > ( NP : <the / DT > < Kellogg _ Center / NNP > )

Examples of Part-of-Speech Tags commonly used in NLP We … … Center

Examples of Open-source NLP Toolkits NLTK - http://nltk.sourceforge.net LingPipe - http://www.alias-i.com/lingpipe/ MII NLP Toolkit - http://www.mii.ucla.edu/nlp/ OpenNLP - http ://opennlp.sourceforge.net/ WARNING ! Advanced Knowledge of Computational Linguistics & Programming Skills Required !!!

NLTK - http://nltk.sourceforge.net

LingPipe - http://www.alias-i.com/lingpipe/

MII NLP Toolkit - http://www.mii.ucla.edu/nlp/

OpenNLP - http ://opennlp.sourceforge.net/

ICTA Internet Community Text Analyzer bulletin board messages Data source Information Organization a nd Access Course name 1200 - 2100 No. of messages per class 31 - 54 No. of students per class 15 weeks Duration of each class 2001 – 2004 School years 8 Classes

Preliminary Exploration of the dataset using ICTA Most frequently used words Important Topics Over Time Community Style Community Support

Most frequently used words

Important Topics Over Time

Community Style

Community Support

Preliminary Exploration 1. Most frequently used words Profession -related words book/s, information, library/libraries, librarian/s user/s, and patron/s, people database/s, search, document/s Learning -related words question/s, article/s, example/s, way, study, class, course, research, journal, reading, method, problem, hard time

Profession -related words

book/s, information, library/libraries, librarian/s

user/s, and patron/s, people

database/s, search, document/s

Learning -related words

question/s, article/s, example/s, way, study, class, course, research, journal, reading, method, problem, hard time

Preliminary Exploration 2. Important Topics Over Time

Preliminary Exploration 3. Community Style

Preliminary Exploration 4. Community Support

Future Work Exploring the use of other word classes (e.g. verbs) Training NLP-algorithms on a CMC-type of corpora Automatic grouping of noun phrases into concepts (with manual override) e.g. RDB, database, relational database Connecting ICTA to external textual data from other public online communities (e.g. blogs, myspace) RSS feeds/ web APIs Understanding the social science of language use in online communities Collaboration with the NCSA’s DISCUS project concept maps social networks

Exploring the use of other word classes (e.g. verbs)

Training NLP-algorithms on a CMC-type of corpora

Automatic grouping of noun phrases into concepts (with manual override)

e.g. RDB, database, relational database

Connecting ICTA to external textual data from other public online communities (e.g. blogs, myspace)

RSS feeds/ web APIs

Understanding the social science of language use in online communities

Collaboration with the NCSA’s DISCUS project

concept maps

social networks