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Impetus Technologies Inc. 
Building a Sentiment Analytics 
Solution Powered by Machine 
© 2014 1 Impetus Technologies 
Lea...
Outline 
• Sentiment Analysis – Why? 
• Solution landscape 
• Addressing challenges with Machine Learning 
• Building a se...
Sentiment Analysis 
• Determines the attitude of a speaker or a writer with 
respect to a particular subject, event or cam...
Measuring Sentiments 
© 2014 4 Impetus Technologies 
Recorded version available at 
http://www.impetus.com/webinar_registr...
Current Solutions Landscape 
• Natural Language Processing 
– Deals with the actual text element 
– Transforms text into a...
Sentiment Analysis: Challenges 
• Demystifying accuracy 
• Inability of machines to gauge and measure sentiments accuratel...
Machine Learning 
Intelligent structure that acquires and integrates 
Learns from experience, training, analytical observa...
Subjectivity Vs Sentiment 
Sentiment analysis - text classification problem 
• Segregate 
• Opinionated documents as per p...
Determining Polarity 
Leveraging Machine Learning 
Algorithm identifies positive/ negative/neutral sentiments 
Refers know...
Predicting Sentiment Intensity 
• Benchmark neutral - say 40-50% of positive is neutral 
– Intensity below the benchmark i...
How it works? 
© 2014 11 Impetus Technologies 
Recorded version available at 
http://www.impetus.com/webinar_registration?...
Text classification –n- gram 
© 2014 12 Impetus Technologies 
Result Report 
Ex: 
RUBBISH – Negative 
RUBBISHING – Negativ...
Building Knowledge Base 
© 2014 13 Impetus Technologies 
Recorded version available at 
http://www.impetus.com/webinar_reg...
Leveraging Machine Learning 
© 2014 14 Impetus Technologies 
Recorded version available at 
http://www.impetus.com/webinar...
Impetus Solution 
• Intuitively retrieves input text for analysis 
• Processes various Low level APIs, REST APIs, enriched...
What’s new? 
Higher accuracy 
Identifies influencers 
Reputation as per demographics 
Measuring sentiment intensity 
© 201...
Case Studies 
Nokia Lumia (On Twitter) 
Apple’s iPad3 (On Facebook) 
© 2014 17 Impetus Technologies 
Recorded version avai...
Case study : Nokia 
• Search keyword - “Nokia Lumia” 
• Total tweets analyzed - 9650 
• Accuracy - 99% 
• Neutral Tweets -...
Case study : Nokia 
• Successful online campaign for Nokia 
© 2014 19 Impetus Technologies 
Recorded version available at ...
Case study : iPad 3 
• Search keyword “ipad3” 
• Total FB status analyzed : 3200 
• Accuracy: 97% 
• Neutral FB Status: 53...
Case Study : iPad 3 
Overall Apple’s performance on online 
campaign was effective 
© 2014 21 Impetus Technologies 
Record...
What It Looks Like? 
© 2014 22 Impetus Technologies 
Recorded version available at 
http://www.impetus.com/webinar_registr...
Summing Up 
• Traditional Approach – NLP and Artificial Intelligence 
• We recommend - A Sentiment Analytics solution base...
© 2014 24 Impetus Technologies 
About Impetus
• Strategic partners for software product engineering and 
R&D 
• Thought leaders in cutting-edge technologies 
• Mature p...
© 2014 26 Impetus Technologies 
Q & A
© 2014 27 Impetus Technologies 
Thank You 
Write to us at inquiry@impetus.com 
Follow us on Twitter @impetustech 
Recorded...
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Building a Sentiment Analytics Solution powered by Machine Learning- Impetus Webinar

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Impetus webcast ‘Building a Sentiment Analytics Solution Powered by Machine Learning’ available at http://lf1.me/I7/sentiment

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Building a Sentiment Analytics Solution powered by Machine Learning- Impetus Webinar

  1. 1. Impetus Technologies Inc. Building a Sentiment Analytics Solution Powered by Machine © 2014 1 Impetus Technologies Learning Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  2. 2. Outline • Sentiment Analysis – Why? • Solution landscape • Addressing challenges with Machine Learning • Building a sentiment analytics solution – Leveraging Machine learning and n-gram • Case Studies © 2014 2 Impetus Technologies Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  3. 3. Sentiment Analysis • Determines the attitude of a speaker or a writer with respect to a particular subject, event or campaign • Computational study of opinions, sentiments, and emotions expressed in text © 2014 3 Impetus Technologies Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  4. 4. Measuring Sentiments © 2014 4 Impetus Technologies Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  5. 5. Current Solutions Landscape • Natural Language Processing – Deals with the actual text element – Transforms text into a format usable by machine • Artificial intelligence – Uses information by NLP and Mathematical calculations – Determines negative, positive or neutral sentiments – Used for clustering © 2014 5 Impetus Technologies Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  6. 6. Sentiment Analysis: Challenges • Demystifying accuracy • Inability of machines to gauge and measure sentiments accurately • Isolating content types • Neutral nature of social media mentions • Sentiment override • User needs override control due to inaccuracy of automated sentiment measurement © 2014 6 Impetus Technologies Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  7. 7. Machine Learning Intelligent structure that acquires and integrates Learns from experience, training, analytical observation © 2014 7 Impetus Technologies knowledge automatically Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  8. 8. Subjectivity Vs Sentiment Sentiment analysis - text classification problem • Segregate • Opinionated documents as per positive/ negative • Sentence or a clause of the sentence as subjective or objective • Subjective sentence or clause on the basis of positive, negative or neutral © 2014 8 Impetus Technologies Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  9. 9. Determining Polarity Leveraging Machine Learning Algorithm identifies positive/ negative/neutral sentiments Refers knowledge bank to determine polarity of a new sentence or word A knowledge bank is the database of pre-trained words and sentences classified © 2014 9 Impetus Technologies as negative, neutral, positive Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  10. 10. Predicting Sentiment Intensity • Benchmark neutral - say 40-50% of positive is neutral – Intensity below the benchmark is negative and above is positive. • Referring Knowledge Bank – Continuously trained by a Machine Learning Algorithm – Intensity predictions becomes accurate Occurrence of a word or sequence of words in a particular polarity decides the © 2014 10 Impetus Technologies intensity of the overall sentiment Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  11. 11. How it works? © 2014 11 Impetus Technologies Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  12. 12. Text classification –n- gram © 2014 12 Impetus Technologies Result Report Ex: RUBBISH – Negative RUBBISHING – Negative RUBBISHED - Negative Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  13. 13. Building Knowledge Base © 2014 13 Impetus Technologies Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  14. 14. Leveraging Machine Learning © 2014 14 Impetus Technologies Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  15. 15. Impetus Solution • Intuitively retrieves input text for analysis • Processes various Low level APIs, REST APIs, enriched XML DOC, Text, © 2014 15 Impetus Technologies Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58 or RSS • Architecture enables exporting services in form of REST APIs • Intuitive solution, capable of processing near real-time data using Big Data stack • Concurrent processing system enables fast results with higher accuracy
  16. 16. What’s new? Higher accuracy Identifies influencers Reputation as per demographics Measuring sentiment intensity © 2014 16 Impetus Technologies Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  17. 17. Case Studies Nokia Lumia (On Twitter) Apple’s iPad3 (On Facebook) © 2014 17 Impetus Technologies Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  18. 18. Case study : Nokia • Search keyword - “Nokia Lumia” • Total tweets analyzed - 9650 • Accuracy - 99% • Neutral Tweets - 42% United Kingdom 23% Germany 3% India 5% France 5% © 2014 18 Impetus Technologies Sentiments: Nokia Lumia 42% 11% 11% Recorded version available at United States 35% http://www.impetus.com/webinar_registration?event=archived&eid=58 Indonesia 15% Italy 8% Mexico 3% Turkey 2% Canada 1% Demographics by –ve sentiment 3% 54% 1% Positive Tweets Negative Tweets 7% 8% 9% 11% 11% 10% 11% 11% United States United Kingdom Indonesia Italy France India Demographics by +ve sentiment
  19. 19. Case study : Nokia • Successful online campaign for Nokia © 2014 19 Impetus Technologies Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58 • Lumia • Pureview • Nokia CRM @nokia (Official Channel) had positive mentions tweets • Reputation very high for all Hashtags of #nokia, #lumia, #pureview • Brand management measured via custom keywords search for nokia with no hash tags - mentions with mostly positive sentiment Reputation Management Brand Management CRM
  20. 20. Case study : iPad 3 • Search keyword “ipad3” • Total FB status analyzed : 3200 • Accuracy: 97% • Neutral FB Status: 53% Demographics: -ve Sentiment © 2014 20 Impetus Technologies Sentiments: Apple’s iPad3 Positive Negative Neutral FP Recorded version available at 48% 53% +ve Sentiment United States Germany France Mexico http://www.impetus.com/webinar_registration?event=archived&eid=58 6% 8% 2% 36% United States Germany France Mexico Canada 31% 13% 3% 23% 3% 4% 1% 17% 52%
  21. 21. Case Study : iPad 3 Overall Apple’s performance on online campaign was effective © 2014 21 Impetus Technologies Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58 • Product: iPad3 Sentiment: Positive • General brand perspective is positive • CRM – Existing customers feel positive Reputation Management Brand Management CRM
  22. 22. What It Looks Like? © 2014 22 Impetus Technologies Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  23. 23. Summing Up • Traditional Approach – NLP and Artificial Intelligence • We recommend - A Sentiment Analytics solution based on Machine Learning, n-gram and Bayes filter classification • Addresses neutral social media mentions, sentiment override, target overlook actual verbatim • Ability to cross-reference intensity, influence trajectory, velocity and sentiment of each social media mention © 2014 23 Impetus Technologies Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  24. 24. © 2014 24 Impetus Technologies About Impetus
  25. 25. • Strategic partners for software product engineering and R&D • Thought leaders in cutting-edge technologies • Mature processes and practices that are methodical, yet flexible • Diverse domain expertise © 2014 25 Impetus Technologies Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58
  26. 26. © 2014 26 Impetus Technologies Q & A
  27. 27. © 2014 27 Impetus Technologies Thank You Write to us at inquiry@impetus.com Follow us on Twitter @impetustech Recorded version available at http://www.impetus.com/webinar_registration?event=archived&eid=58

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