Text Mining TripAdvisor Data in search of ethnic ...files.meetup.com/16175222/Text Mining SF...

Copyright © 2014 KNIME.com AG

Text Mining TripAdvisor Datain search of ethnic Restaurants in San Francisco

May, 2016

Rosaria Silipo

KNIME.com AG, Zurich, Switzerland

www.knime.com

@KNIME

[email protected]

http://www.knime.com/

mailto:[email protected]


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


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:1. Import Data 2. Enrichment (Tagging)

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

4. TransformationBoW, Frequencies,Document Vector

5. ClassificationClustering


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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1.) Reading

Read/Parse textual data

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0.) Web Crawler Workflow

Palladian Extension from:

KNIME Community Contributions – Other

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Html Parser XPath


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


3.) Preprocessing

Preprocess documents and filter words

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Filter Numbers, Punctuation, Stop Words

to lowercase

Stemming


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


Frequencies

Frequency Calculation

• 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, top words should be most important

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Tag Cloud for Italian Restaurants

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Tag Cloud for Chinese Restaurants

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4.) Transformation

Creation of numerical representation of documents

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Transform to Numerical Data Tables

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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Decision Tree Insights

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Discriminative Words:

• Italian vs. Chinese

• Pizza, Pasta, Wine vs. Sum, Soup

• Chinatown, China vs. Beach


5.) X-Validation Loop

Applying the Cross-Validation Loop (10 folds)

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Error Stats from X-Validation Loop

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Error Stats from the Statistics Node:


Topic Extraction (Topic Extractor node)

Using “Topic Extractor (Parallel LDA)” node with 10 words to describe each topic

Simple parallel threaded implementation of LDA, from:

Newman, Asuncion, Smyth and Welling, Distributed Algorithms for Topic Models with SparseLDA sampling scheme and data structure, JMLR (2009) Yao, Mimno and McCallum, Efficient Methods for Topic Model Inference on Streaming Document Collections, KDD (2009).

The node uses the "MALLET: A Machine Learning for Language Toolkit." topic modeling library.

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http://en.wikipedia.org/wiki/Latent_Dirichlet_allocation

http://machinelearning.wustl.edu/mlpapers/paper_files/jmlr10_newman09a.pdf

http://www.cs.umass.edu/~lmyao/papers/fast-topic-model10.pdf

http://mallet.cs.umass.edu/


Introducing NGrams (NGram Creator node)

• Multi Word Tagging

– Detection of frequent Ngrams (Ngram Creator)

– Creation of dictionary from Ngrams

– Applying Dictionary Tagger

• Classification with Multi Words (X-Validation Loop):

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Higher average error

Lower variance


Thank You

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• [email protected]

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