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Sentiment Analysis - Why You should Do it and How you can do it

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Sentiment Analysis is the technique to analyze the sentiments of people from text. In this presentation, you learn about the business cases of sentiment analysis and why sentiment analysis is helpful to improve products, enable closed-loop customer experience, and gain competitive advantage through intelligence. Learn how how you can do quickly sentiment analysis based on existing cloud services.

Published in: Data & Analytics
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Sentiment Analysis - Why You should Do it and How you can do it

  1. 1. SENTIMENT ANALYSIS Why and How Swiss Group for Artificial Intelligence and Cognitive Science - 2nd SGAICO at HSLU – May 23rd, 2017 https://sgaico.swissinformatics.org/
  2. 2. Who am I Dr. Amancio Bouza Intrapreneur & Artificial Intelligence Expert Let’s connect: https://ch.linkedin.com/in/amanciobouza Follow me: https://twitter.com/AmancioBouza
  3. 3. 3 WHY?Do we want Sentiment Analysis
  4. 4. 4 Identify (un)happy customers?
  5. 5. Simple Alternative: Net Promoter Score (NPS) How likely do you recommend Travelito to your friends? 5 very unlikely very likely
  6. 6. 6 So, WHY then?
  7. 7. 7 1. Improve Products 2. Enable Closed-Loop Customer Experience 3. Understand Competition
  8. 8. 8 How to improve Products
  9. 9. 9 “The bed in the City Hotel was not comfortable. However, the breakfast was excellent with good food and excellent service. We had a wonderful time and will travel with Travelito again.” Example of customer feedback for anonymized company in tourism industry
  10. 10. Analyze Entities Related to Negative Feedbacks and Drill Down 10 Named Entity Recognition: - Organization - Location - Person Can you spot the problem? ;)
  11. 11. 11 Enable Closed-Loop Customer Experiences
  12. 12. React in Real-Time to Customer Disatisfaction 12 End-Customers tell you if they are unhappy Rest assured they tell the world too!
  13. 13. 13 Happy Customers Come Back Happy Customers Become Fans and Spread the Word
  14. 14. Use Social Media Channels for Real-Time Sentiment Analysis 14 Mitigate Dissatisfaction at the right time Keep retention (loyalty) and referrals high
  15. 15. 15 Competition Intelligence
  16. 16. Identify Strength and Weaknesses of Competition 16 Find out Competitive Advantage of your Competition… …and Make it Better or Make it Irrelevant
  17. 17. Find Info on Review Platforms 17
  18. 18. 18 WHAT?Is the Challenge
  19. 19. Customers do provide feedback, lots of feedback
  20. 20. … and that’s GREAT! 20
  21. 21. But How to Process so Many Customer Feedbacks? 21
  22. 22. 22 HOW?To do Sentiment Analysis
  23. 23. Let’s try Sentiment Analysis with Machine Learning
  24. 24. 24 Disclaimer Sentiment Analysis PoC was done in few days No extra Cognitive Services used besides ML Studio Dataset consisted of 40k customer feedbacks Prior-Probability of negative Feedback ~45% Evaluation was done with 10-fold Cross Validation
  25. 25. Proposal 25 Use whatever you want
  26. 26. Following the Lean Approach Follow the Lean Approach CRISP-DM Cross Industry Standard Process for Data Mining 26
  27. 27. 27
  28. 28. Sentiment Analysis PoC in a Nutshell Data Cleaning Remove uncomplete data Data Preparation What to learn , i.e., sentiment How to learn, i.e., cognitive computing Machine Learning Learn to identify sentiments Sentiment Analysis Identify sentiments in customer feedback 28
  29. 29. 29 Common Text Preprocessing applied - Lemmatization - Normalize Lower Case - and much more Best Performer for this dataset Two-Class Boosted Decision Tree 500 Features were selected for this dataset selected with 𝜒2 Extraction of N-Gram Features from Text
  30. 30. Key Experiences with Azure ML Studio for Sentiment Analysis + 1-Gram Features provided best results + Preprocessing and Normalization of Text is simple - Data Preparation was cumbersome with built-in function blocks -> use R - Key Phrase Extraction was useless to extract sentiments - Handover of ML Workspace to others is complicated - Automated creation of Web Service isn’t intuitive. Requires rethinking of the ML workflow + However, it provides great API Documentation, Sandbox and examples on the fly + Intuitive Machine Learning Studio + Fast & simple setup of (scientifically valid) experiments + High Computational Performance + Support for R / R required for advanced stuff, data preparation in particular 30
  31. 31. 31 WHAT?Is the Result
  32. 32. 32 “The bed in the City Hotel was not comfortable. However, the breakfast was excellent with good food and excellent service. We had a wonderful time and will travel with Travelito again.” Example of customer feedback for anonymized company in tourism industry
  33. 33. Sentiment Analysis PoC ranks with human to interpret sentiment 33 79% Accuracy
  34. 34. Evaluation results in detail: 79% Accuracy 34 Accuracy Precision Recall F-Score AUC 0.788 0.751 0.733 0.742 0.853
  35. 35. Take Home Message: WHY do we do Sentiment Analysis 35 Improve Products Closed-Loop Customer Experience Competition Intelligence
  36. 36. Who am I Dr. Amancio Bouza Intrapreneur & Artificial Intelligence Expert Let’s connect: https://ch.linkedin.com/in/amanciobouza Follow me: https://twitter.com/AmancioBouza

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