SF Data Mining Meetup September 22, 2014dataminingreporting.weebly.com/uploads/4/0/9/7/4097240/...SF...

9/23/2014

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Copyright © 2014 KNIME.com AG

Text Analytics TutorialSF Data Mining MeetupSeptember 22, 2014

Kilian Thiel, Rosaria Silipo, Cathy Pearl

KNIME.com AG, Zurich, Switzerland

www.knime.com

@KNIME

[email protected]

[email protected]

[email protected]


Tool Installation

• Download open source KNIME analytics platform from:

http://www.knime.org/knime-analytics-platform-sdk-download

• Select package for your OS and install

• Open the KNIME application

• In the top menu select “File” or “LOCAL” -> “Install KNIME Extensions”

• Install “KNIME & Extensions” and “KNIME Labs Extensions”

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http://www.knime.com/

mailto:[email protected]



http://www.knime.org/knime-analytics-platform-sdk-download

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Install KNIME Extensions (incl. Text Processing)

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Requirements to import and run Demo Workflows

• KNIME 2.10

• Text Processing Extension from KNIME Labs Extensions

• Distance Matrix from KNIME Extensions

Memory Tip

In file knime.ini set memory to max available

• -Xmx 3G

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• The KNIME Website (www.knime.org)• LEARNING HUB under RESOURCES (www.knime.org/learning-

hub)

• Use Cases and White Papers for example workflows, and

• FORUM for questions and answers

• DOCUMENTATION for documentation, FAQ, change-logs, ...

• LABS for new developments and experimental nodes

• COMMUNITY for development instructions and third party nodes

• Blog for news, tips and tricks(www.knime.org/blog)

• KNIME TV channel on

Text Mining Webinar http://www.youtube.com/watch?v=tY7vpTLYlIg

• KNIME on @KNIME

Resources

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Resources

eBooks from the KNIME Press:

http://www.knime.org/knimepress

- KNIME Beginner’s Luck

- The KNIME Cookbook

- The KNIME Booklet for SAS Users

Free Beginner’s Guide – use Code

“meetupsf14”

http://www.knime.org/

http://www.knime.org/learning-hub

http://www.knime.org/blog

http://www.youtube.com/watch?v=tY7vpTLYlIg

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Text Processing Steps

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1. Import Data

2. Enrichment(Tagging)

3. Pre-processing(Filtering, Stemming, …)

4. TransformationBoW, Frequencies,Document Vector

4. ClassificationClustering

Document Type Cell

Term Type Cell


Import Demo Workflows

• Download zip file with demo workflows from meetup site

• Open the KNIME application

• In the top menu, select File -> Import KNIME Workflow ...

• Enable option „Select Archive File“

• Browse to zip file

• Import all workflows and data into KNIME

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Import Demo Workflows

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Demo Workflows

0-TripAdvisorCrawling: importing data from web

1-Reading: Importing data from text, word, pdf, Twitter, XML, …

2-Enrichment POS: String to Document and Word Tagging in Document

3-Preprocessing: Filtering and Stemming

4-Classification-Cuisine: BoW, Frequencies, Document to Document Vector

Other workflows for multi-words, clustering, topic extraction, and reporting.

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Demo: The KNIME Workbench


Text Processing Category

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Demo: TripAdvisor Restaurant Data Set (SF)

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Demo: TripAdvisor Data (SF Restaurants)

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Reviews about Italian and Chinese restaurants in San Francisco

• Chinese: 272

• Italian: 268

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Demo: Goal of this Tutorial

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Goal:

• Build a classifier to distinguish between Chinese and Italian restaurants, based on the reviews.

Italian or Chinese Restaurant?


Demo: Final Workflow

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Goal:

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1.) Reading

Read/Parse textual data

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Demo

Reading

• Read Tripadvisor data (.table file)

• Filter rows with missing restaurant value

• Convert strings to documents

• Filter all but the document column

• Examples of other possible formats to import

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0.) Web Crawler Workflow

Palladian Extension from:

KNIME Community Contributions – Other

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Demo

Reading

• Web Crawler Workflow to get data from the Web

• Palladian Community Contributions Extension

• HtmlParser node

• Xpath node

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2.) Enrichment

Enrich documents with semantic information

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This assigns a tag to each word:- Grammar tags (POS)- Context dependent tags- Sentiment tags- Named Entity tags - Custom tags


Demo

Enrichment / Tagging

• Apply POS Tagger node

• Use Bag of Words node to inspect tagging result

• Show other possible Taggings

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3.) Preprocessing

Preprocess documents and filter words

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Demo

Preprocessing

• Filter

– Numbers

– Punctuation marks

– Stop Words

• Convert to lower case

• Stemming (Snowball stemmer because of the many languages associated with it)

• Keep only nouns (NN), verbs (VB), adjectives (JJ)

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4.) Transformation

Creation of numerical representation of documents

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BoW creates the list of words for each documentTF calculates word frequencies (absolute or relative)

in each document


Demo

Transformation

• Transform to bag of word

• Compute TF value for terms

TFrel (word) = n(word)/N

IDF(word) = log(1+(n(docs)/n(word, docs))

Tfrel(word) * IDF(word) is used often

ICF(word) = log(1+(n(cat)/n(word, cat))

• Sort output data by frequency

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4.) Transformation

Creation of numerical representation of documents

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Demo

Transformation

• Transform to document vectors

• Extract category (class) value

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5.) Classification

Back to classical Data Analytics:

Training of a model (decision tree) and scoring

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Demo

Classification

• Append color based on class

• Partition data into training and test set

• Train decision tree model in training data

• Apply decision tree model on test data

• Score model, measure accuracy

• Show cross-validation loop

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Additional Workflows

• Multi Word Tagging

– Detection of frequent Ngrams (Ngram Creator)

– Creation of dictionary from Ngrams

– Applying Dictionary Tagger

• Classification with Multi Words

• Clustering of documents

– hierarchical clustering based on distance matrix

• Topic Extraction

– Topic Extractor (Parallel LDA)

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Thank You

40k

60k

20k

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Questions

• http://tech.knime.org/forum

• [email protected]

Follow us

• Twitter: @KNIME

• LinkedIn: https://www.linkedin.com/groups?gid=2212172

• KNIME Blog: http://www.knime.org/blog

http://tech.knime.org/forum


https://www.linkedin.com/groups?gid=2212172

http://www.knime.org/blog

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