Sentiment Analysis with Deep Learning, Machine Learning or Lexicon based

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This document discusses different approaches to sentiment analysis: lexicon-based, machine learning, and deep learning. It provides an overview of how to perform sentiment analysis using the KNIME analytics platform, including reading and parsing data, enriching documents with semantic information, preprocessing documents, computing frequencies, transforming data for classification models, and creating workflows for each approach. The document encourages attendees to download a free book on text analytics from the KNIME Press using a provided code.

Data & Analytics

© 2018 KNIME AG. All rights reserved.
Sentiment Analysis with
Deep Learning, Machine Learning or
Lexicon Based

© 2018 KNIME AG. All rights reserved. 2
Sentiment Analysis – An Example

© 2018 KNIME AG. All rights reserved. 3
Sentiment Analysis
Task: Determine the expressed opinion in a document/text, e.g.
positive, negative
Sentiment Analysis = Opinion Mining = Emotion AI
Lexicon Based Machine Learning Deep Learning

© 2018 KNIME AG. All rights reserved. 4
Philosophy
… perhaps your
name is
Rumpelstiltskin[Pers
on] ? …
… perhaps your
name is
Rumpelstiltskin[Per
son] ? …
Visualizatio
n
Cluster
-ing
Classifi-
cation
Reading/Parsing Data Enrichment Preprocessing
… perhaps your
name is
Rumpelstiltskin[Per
son] ? …
Transformations / Frequencies Classification/Clustering/Visualization

© 2018 KNIME AG. All rights reserved. 5
Additional Data Types
• Document Cell
– Encapsulates a document
• Title, sentences, terms, words
• Authors, categories, sources
• Generic meta data (key, value pairs)
• Term Cell
– Encapsulates a term
• Words, tags

© 2018 KNIME AG. All rights reserved. 6
What is KNIME Analytics Platform?
• A tool for data analysis, manipulation, visualization, and reporting
• Based on the graphical programming paradigm
• Provides a diverse array of extensions:
• Text Mining
• Network Mining
• Cheminformatics
• Many integrations
such as Java, R, Python,
Weka, H2O, etc.
6

© 2018 KNIME AG. All rights reserved. 10
Part 1: Reading and Parsing Data
Read/Parse textual data
Other Reader nodes

© 2018 KNIME AG. All rights reserved. 11
Part 2: Enrichment
Enrich documents with semantic information

© 2018 KNIME AG. All rights reserved. 13
Part 3: Preprocessing
Preprocess documents and filter words

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Bag of Words
• A so-called ‘Bag of Words’ represents each document as the bag
(multiset) of its words
• Grammar and word order aren’t taken into account

© 2018 KNIME AG. All rights reserved. 15
Frequency Nodes
• Node Repository:
Other Data Types /Text Processing /Frequencies
• Available Frequency Nodes
– TF
– IDF
– Ngram creator
– …

© 2018 KNIME AG. All rights reserved. 16
Document Vector
• Transforms a bag of words into a sequence
– of 0/1 (“one-hot encoding”)
– or frequency numbers
16
BoW with Frequency
Column
Document
Vector

© 2018 KNIME AG. All rights reserved. 17
Part 4: Transformation and Frequencies’ Computation
Preprocess documents

© 2018 KNIME AG. All rights reserved. 18
Part 5: Classification
Lexicon based Machine learning
v

© 2018 KNIME AG. All rights reserved. 19
Transformation for Deep Learning
Expected input of a network:
• Numerical representation of each document encoding the words and their order
• Equivalent input shape of each document
– Truncate too long documents
– Zero pad too short documents

© 2018 KNIME AG. All rights reserved. 20
Approach 3: Deep Learning

© 2018 KNIME AG. All rights reserved. 21
The Workflows

© 2018 KNIME AG. All rights reserved. 23
From Words to Wisdom Book
Free Copy of “From Words to Wisdom” Book from KNIME Press
https://www.knime.com/knimepress
with code: USMEETUPS-0918

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Sentiment Analysis with Deep Learning, Machine Learning or Lexicon based

3. © 2018 KNIME AG. All rights reserved. 3 Sentiment Analysis Task: Determine the expressed opinion in a document/text, e.g. positive, negative Sentiment Analysis = Opinion Mining = Emotion AI Lexicon Based Machine Learning Deep Learning

4. © 2018 KNIME AG. All rights reserved. 4 Philosophy … perhaps your name is Rumpelstiltskin[Pers on] ? … … perhaps your name is Rumpelstiltskin[Per son] ? … Visualizatio n Cluster -ing Classifi- cation Reading/Parsing Data Enrichment Preprocessing … perhaps your name is Rumpelstiltskin[Per son] ? … Transformations / Frequencies Classification/Clustering/Visualization

5. © 2018 KNIME AG. All rights reserved. 5 Additional Data Types • Document Cell – Encapsulates a document • Title, sentences, terms, words • Authors, categories, sources • Generic meta data (key, value pairs) • Term Cell – Encapsulates a term • Words, tags

6. © 2018 KNIME AG. All rights reserved. 6 What is KNIME Analytics Platform? • A tool for data analysis, manipulation, visualization, and reporting • Based on the graphical programming paradigm • Provides a diverse array of extensions: • Text Mining • Network Mining • Cheminformatics • Many integrations such as Java, R, Python, Weka, H2O, etc. 6

12. © 2018 KNIME AG. All rights reserved. 16 Document Vector • Transforms a bag of words into a sequence – of 0/1 (“one-hot encoding”) – or frequency numbers 16 BoW with Frequency Column Document Vector

15. © 2018 KNIME AG. All rights reserved. 19 Transformation for Deep Learning Expected input of a network: • Numerical representation of each document encoding the words and their order • Equivalent input shape of each document – Truncate too long documents – Zero pad too short documents

19. 26© 2018 KNIME AG. All rights reserved. The KNIME® trademark and logo and OPEN FOR INNOVATION® trademark are used by KNIME.com AG under license from KNIME GmbH, and are registered in the United States. KNIME® is also registered in Germany.

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