1 I256: Applied Natural Language Processing Marti Hearst Nov 8, 2006.

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I256: Applied Natural Language Processing

Marti HearstNov 8, 2006

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Today

Comparing term clustering and category outputClustering in WekaData mining from blogs

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LDA

Latent Dirchelet AllocationBlei, Ng, Jordan, JLMR 03.LDA is a hierarchical probabilistic model of documents. “LDA allows you to analyze of corpus, and extract the topics that combined to form its documents.”http://www.cs.princeton.edu/~blei/lda-c/Not really clustering, but in the “soft clustering” ballpark.

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LDA on Recipeshttp://orange.sims.berkeley.edu/cgi-bin/flamenco.cgi/recipes-newblei/

Flamenco

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LDA on Recipeshttp://orange.sims.berkeley.edu/cgi-bin/flamenco.cgi/recipes-newblei/

Flamenco

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CastaNet

(Semi)automated facet creationStoica & HearstBuild up from WordNetAlgorithm is fully automatic but we think you can improve results manually afterwards.

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CastaNet on Recipeshttp://orange.sims.berkeley.edu/cgi-bin/flamenco.cgi/recipes-automated/

Flamenco

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CastaNet on Recipeshttp://orange.sims.berkeley.edu/cgi-bin/flamenco.cgi/recipes-automated/

Flamenco

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TopicSeek on Enron EmailTechnique: pLSI (probabilistic LSI, Hofmann 99)Hand-picked example for websitehttp://topicseek.com/enron.html

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TopicSeek on MedlineTechnique: pLSI (probabilistic LSI, Hofmann 99)Hand-picked example for websitehttp://topicseek.com/pubmed.html

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CastaNet on Medline Journal Titleshttp://orange.sims.berkeley.edu/cgi-bin/flamenco.cgi/medicine-automated/

Flamenco

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Clustering in Weka

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Looking at Clustering Results

Weka lets you save cluster results to an ARFF fileI wrote some python code to process this file and pull out the Subject headings for each newsgroup posting in each cluster.

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15-way clustering

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Cobweb clustering

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Blog Analysis

What’s special about blogs?

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Blog analysis sites

http://dijest.com/bc/Called blogcount; lots of stats and news about blogs

http://blogcensus.net/?page=toolsLanguage, location, marketshare

http://www.perseus.com/blogsurvey/Stats about biggest blogs, demographics

http://www.weblogs.com/Notify when new content posted

http://blogpulse.com/Trends and recent popular topics

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Blogs vs. Newsgroups

Posting about products … what can we tell?Blog:

Newsgroup:

Example from Glance, Hurst, and Tomokiyo ‘04

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Analyzing Blogs for Market Data

Figure from Glance, Hurst, Nigam, Siegler, Stockton, & Tomokiyo, KDD’05

Idea: examine comments about a product (or a product’s competition or market) in an automated fashion.Application area: handheld electronic devices.

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Analyzing Blogs for Market Data

Figure from Glance, Hurst, Nigam, Siegler, Stockton, & Tomokiyo, KDD’05

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Technology usedPost segmentationImportant phrases

Foreground vs. background corpus– Background: text about product– Foreground: certain negative paragraphs about product

Sentiment classificationWhat do people talk about when saying negative things about product X?

Social network analysis (on discussion boards)What does this group of people talk about when saying negative things about product X?Author dispersion

– Many people talking about it, or just a few?

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Example

What common phrases to people use when saying negative things about product X?

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ExampleWhat do people in this group say when saying negative things about product X?

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Example

What do people in this group say when saying negative things about product X?

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Predicting Film Sales

Idea: Use discussion before a film to predict its opening weekend box office scoresUse discussion afterwards to predict longer-term salesSeparate out topic labels from sentiment labels

Outcome:Good predictor for opening weekend, but not for longer termObservation: the nature of discussion gets (and thus harder to analyze) after the film has been out a while.

Example from Mishne & Glance, 2006

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Prediction Film Sales


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Analyzing Political Blogs

Analyze:Who links to whomWhat the popularity profile looks like

– A powerlaw/Zipf/Pareto, of course

Look at structure of topic-specific blogs

– By #inbound links

Image from blogsphere ecosystem via Shirky

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Earlier work examined books bought together in pairs at major retailers

Krebs, Divided we Stand??? http://www.orgnet.com/leftright.html

In other domains the groupings are more distributed.

36 http://www.orgnet.com/booknet.html

37 http://www.orgnet.com/leftright.html from Jan 2003

38 http://www.orgnet.com/divided.html from 2004 election

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Study by Adamic and Glance, 2005Analyzed 40 most popular political blogs2 months preceding 2004 US presidential electionAlso study 1000 political blogs on a one day snapshotFindings for the latter:

Liberal and conservative blogs had distinct lists of favorate news sources, people, and topics, with some overlap on current news

– Use labels from aggregator sources

Linking patterns were indeed pretty internal (91% stayed within political leaning)More and more frequent linking among conservatives

– 82% conservative linked out vs. 74% of liberal

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For the 40 most popular blogs:Looked for “echo chamber” effect

The conservative blogs are more tightly interlinked.Question: do they repeat the same concepts more?

– Measured textual similarity among blog posts– Slightly stronger within a political leaning than

between, but not one orientation more than the other.

Looked for interaction with “mainstream” media

Found strong distinctions between which sources cited

41 Image from Adamic & Glance 200

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Next Time

Sentiment and Opinion Analysis

1 I256: Applied Natural Language Processing Marti Hearst Nov 8, 2006.

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Transcript of 1 I256: Applied Natural Language Processing Marti Hearst Nov 8, 2006.