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Sentiment Analysis with KNIME Analytics Platform

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KNIMESlides
KNIMESlides

“Great movie with a nice story!” What do you think, did the person like the film or hate it? Most of the time it’s easy for us to decide whether the message of a text is positive or negative. But what if you wanted to automate the process of understanding the sentiment? For example, if you have a lot of customers leaving comments, or people publishing movie reviews, you will want to discern the sentiment and find out who is posting positive or negative messages. Sentiment analysis is an important piece of many data analytics use cases. Whether it processes customer feedback, movie reviews, or tweets, sentiment scores often contribute an important piece to describing the whole scenario. These are just some examples of a long list of use cases for sentiment analysis, which includes social media analysis, 360 degree customer views, customer intelligence, competitive analysis and many more. To avoid doing this manually, we apply sentiment analysis and teach an algorithm to understand text and extract the sentiment using Natural Language Processing. A copy of the webinar can be viewed at https://www.youtube.com/watch?v=By4IZeIzxIw

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© 2018 KNIME AG. All rights reserved. Sentiment Analysis with Deep Learning, Machine Learning or Lexicon Based Kathrin Melcher
© 2018 KNIME AG. All rights reserved. 2 Sentiment Analysis – An Example
© 2018 KNIME AG. All rights reserved. 3 Sentiment Analysis – Some Use Cases • Movies: is this review positive or negative? • Social Media Monitoring: is this tweet positive or negative? • Politics: what do people think about a specific candidate or political issue? • Customer Experience: what do people think about a new smartphone?
© 2018 KNIME AG. All rights reserved. 4 Sentiment Analysis – Examples • “I like to travel.” • “I like to travel, sometime.” • “I do not dislike cabin cruisers.” • “It looks like it's going to rain.”
© 2018 KNIME AG. All rights reserved. 5 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 Based
© 2018 KNIME AG. All rights reserved. 6 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. 7 Lexicon Based / Rule Based Approach Read Text Read Dictionaries Define Rules Evaluate #Positive Words #Negative Words Tag Positive Words Tag Negative Words Preprocess & Transform , 𝑠𝑒𝑛𝑡𝑖𝑚𝑒𝑛𝑡 𝑠𝑐𝑜𝑟𝑒 = # 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑤𝑜𝑟𝑑𝑠−# 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑤𝑜𝑟𝑑𝑠 #𝑡𝑜𝑡𝑎𝑙 𝑤𝑜𝑟𝑑𝑠 sentiment score >0 => positive sentiment score <= 0 => negative
© 2018 KNIME AG. All rights reserved. 9 Rule-based Classification with Dictionary Tagger
© 2018 KNIME AG. All rights reserved. 11 Machine Learning Based Approach Read Labeled Text Evaluate Feature Extraction Train & Apply Model Read Text Feature Extraction Apply Model Training Workflow Deployment Workflow Labels
© 2018 KNIME AG. All rights reserved. 12 Machine Learning Based Approach Blog post: https://www.knime.com/blog/sentiment-analysis-with-n-grams
© 2018 KNIME AG. All rights reserved. 14 Machine Learning Using Deep Learning
© 2018 KNIME AG. All rights reserved. 15 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. 16 Installation For Deep Learning • KNIME Integrations – KNIME Python Integration – KNIME Deep Learning – Keras Integration – KNIME Deep Learning – TensorFlow Integration • Python environment with Keras and TensorFlow – https://www.knime.com/deeplearning/keras – Recommended: keras = 2.1.6 tensorflow = 1.8.0 • Training on GPU: keras-gpu and tensorflow-gpu
© 2018 KNIME AG. All rights reserved. 17 From Words to Wisdom Book Free Copy of “From Words to Wisdom” Book from KNIME Press https://www.knime.com/knimepress with code: SENTIMENT-ANALYSIS-WEBINAR-1118 Thank you for attending todays webinar!
18© 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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Sentiment Analysis with KNIME Analytics Platform

  • 1. © 2018 KNIME AG. All rights reserved. Sentiment Analysis with Deep Learning, Machine Learning or Lexicon Based Kathrin Melcher
  • 2. © 2018 KNIME AG. All rights reserved. 2 Sentiment Analysis – An Example
  • 3. © 2018 KNIME AG. All rights reserved. 3 Sentiment Analysis – Some Use Cases • Movies: is this review positive or negative? • Social Media Monitoring: is this tweet positive or negative? • Politics: what do people think about a specific candidate or political issue? • Customer Experience: what do people think about a new smartphone?
  • 4. © 2018 KNIME AG. All rights reserved. 4 Sentiment Analysis – Examples • “I like to travel.” • “I like to travel, sometime.” • “I do not dislike cabin cruisers.” • “It looks like it's going to rain.”
  • 5. © 2018 KNIME AG. All rights reserved. 5 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 Based
  • 6. © 2018 KNIME AG. All rights reserved. 6 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
  • 7. © 2018 KNIME AG. All rights reserved. 7 Lexicon Based / Rule Based Approach Read Text Read Dictionaries Define Rules Evaluate #Positive Words #Negative Words Tag Positive Words Tag Negative Words Preprocess & Transform , 𝑠𝑒𝑛𝑡𝑖𝑚𝑒𝑛𝑡 𝑠𝑐𝑜𝑟𝑒 = # 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑤𝑜𝑟𝑑𝑠−# 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑤𝑜𝑟𝑑𝑠 #𝑡𝑜𝑡𝑎𝑙 𝑤𝑜𝑟𝑑𝑠 sentiment score >0 => positive sentiment score <= 0 => negative
  • 8. © 2018 KNIME AG. All rights reserved. 9 Rule-based Classification with Dictionary Tagger
  • 9. © 2018 KNIME AG. All rights reserved. 11 Machine Learning Based Approach Read Labeled Text Evaluate Feature Extraction Train & Apply Model Read Text Feature Extraction Apply Model Training Workflow Deployment Workflow Labels
  • 10. © 2018 KNIME AG. All rights reserved. 12 Machine Learning Based Approach Blog post: https://www.knime.com/blog/sentiment-analysis-with-n-grams
  • 11. © 2018 KNIME AG. All rights reserved. 14 Machine Learning Using Deep Learning
  • 12. © 2018 KNIME AG. All rights reserved. 15 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
  • 13. © 2018 KNIME AG. All rights reserved. 16 Installation For Deep Learning • KNIME Integrations – KNIME Python Integration – KNIME Deep Learning – Keras Integration – KNIME Deep Learning – TensorFlow Integration • Python environment with Keras and TensorFlow – https://www.knime.com/deeplearning/keras – Recommended: keras = 2.1.6 tensorflow = 1.8.0 • Training on GPU: keras-gpu and tensorflow-gpu
  • 14. © 2018 KNIME AG. All rights reserved. 17 From Words to Wisdom Book Free Copy of “From Words to Wisdom” Book from KNIME Press https://www.knime.com/knimepress with code: SENTIMENT-ANALYSIS-WEBINAR-1118 Thank you for attending todays webinar!
  • 15. 18© 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.