Big Data. Daniel Hardt. Supply Chain Leaders Forum 3 September IT Management, CBS

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1 The Revolution Learning from : Text, Feelings and Machine Learning IT Management, CBS Supply Chain Leaders Forum 3 September 2015

2 The Revolution Learning from : Text, Feelings and Machine Learning Outline 1 The Revolution Watson Google Self-Driving Car 2 Learning from : Text, Feelings and Machine Learning Sentiment Analysis Mining Facebook for Feelings 3 Big Transportation and Trade Data Analytics at Roskilde Festival 4

3 The Revolution Learning from : Text, Feelings and Machine Learning Increasing Availability of Data Watson Google Self-Driving Car

4 The Revolution Learning from : Text, Feelings and Machine Learning Data Challenges Watson Google Self-Driving Car

5 The Revolution Learning from : Text, Feelings and Machine Learning Characteristics Watson Google Self-Driving Car

6 The Revolution Learning from : Text, Feelings and Machine Learning Lots of Photos Watson Google Self-Driving Car

7 The Revolution Learning from : Text, Feelings and Machine Learning Lots of Photos Watson Google Self-Driving Car

8 The Revolution Learning from : Text, Feelings and Machine Learning : Definition Watson Google Self-Driving Car Big data data sets so large or complex that traditional data processing applications are inadequate. (Wikipedia)

9 The Revolution Learning from : Text, Feelings and Machine Learning Is this Surprising? Watson Google Self-Driving Car Moore s law: computing power doubles every 2 years (roughly)

10 The Revolution Learning from : Text, Feelings and Machine Learning Is this True? Watson Google Self-Driving Car Big data data sets so large or complex that traditional data processing applications are inadequate. (Wikipedia) Increase of data is keeping pace with processing power In fact, increase in data is itself supporting new ways to process data Artificial Intelligence

11 The Revolution Learning from : Text, Feelings and Machine Learning Watson Google Self-Driving Car Watson: The Jeopardy Challenge

12 The Revolution Learning from : Text, Feelings and Machine Learning Watson: Jeopardy Watson Google Self-Driving Car

13 The Revolution Learning from : Text, Feelings and Machine Learning Jeopardy is Hard! Watson Google Self-Driving Car

14 The Revolution Learning from : Text, Feelings and Machine Learning Watson: Health Care Watson Google Self-Driving Car

15 The Revolution Learning from : Text, Feelings and Machine Learning IBM Evolution of Computing Watson Google Self-Driving Car

16 The Revolution Learning from : Text, Feelings and Machine Learning Cognitive Computing Watson Google Self-Driving Car

17 The Revolution Learning from : Text, Feelings and Machine Learning Google Self-Driving Car Watson Google Self-Driving Car

18 The Revolution Learning from : Text, Feelings and Machine Learning Watson Google Self-Driving Car The Google Car s View of the World Madrigal (2014), Atlantic

19 The Revolution Learning from : Text, Feelings and Machine Learning Watson Google Self-Driving Car The Google Car s View of the World Madrigal (2014), Atlantic

20 The Revolution Learning from : Text, Feelings and Machine Learning The Trick: Crawling the World Watson Google Self-Driving Car Google wants to make the self-driving car problem into a problem Car has ultra-detailed map for every road it travels on, down to tiny details like the position and height of every single curb... a precision measured in inches Google has mapped 2,000 miles of road. The US road network has 4 million miles of road. It is work, Urmson added, shrugging, but it is not intimidating work. Madrigal (2014), Atlantic

21 The Revolution Learning from : Text, Feelings and Machine Learning Sentiment Analysis Mining Facebook for Feelings Liu, Bing. Sentiment analysis and subjectivity Handbook of natural language processing 2 (2010): 568. Sentiment analysis or opinion mining is the computational study of opinions, sentiments and emotions expressed in text Lots of Buzz!

22 The Revolution Learning from : Text, Feelings and Machine Learning Business Sentiment Analysis Mining Facebook for Feelings

23 The Revolution Learning from : Text, Feelings and Machine Learning Business Sentiment Analysis Mining Facebook for Feelings

24 The Revolution Learning from : Text, Feelings and Machine Learning Business Sentiment Analysis Mining Facebook for Feelings

25 The Revolution Learning from : Text, Feelings and Machine Learning Business Sentiment Analysis Mining Facebook for Feelings

26 The Revolution Learning from : Text, Feelings and Machine Learning Business Sentiment Analysis Mining Facebook for Feelings

27 The Revolution Learning from : Text, Feelings and Machine Learning Machine Learning Methods Sentiment Analysis Mining Facebook for Feelings Pang, Bo, Lillian Lee, and Shivakumar Vaithyanathan. "Thumbs up?: sentiment classification using machine learning techniques." Proceedings of the ACL-02 conference on Empirical methods in natural language processing-volume 10. Association for Computational Linguistics, Bag of words: With lexicon of m words, each document d is represented by the document vector (n 1 (d), n 2 (d),..., n m (d)) Machine Learning: Naive Bayes, Maximum Entropy, Support Vector Machines Naive Bayes:

28 The Revolution Learning from : Text, Feelings and Machine Learning Data: Facebook Feelings Sentiment Analysis Mining Facebook for Feelings

29 The Revolution Learning from : Text, Feelings and Machine Learning Arousal and Valence: Data Sentiment Analysis Mining Facebook for Feelings

30 The Revolution Learning from : Text, Feelings and Machine Learning Five Basic Feelings: Data Animated Excited Pumped 2979 Surprised 752 Amused Joy Happy Wonderful Awesome Super 5794 Great Fantastic 3596 Delighted 805 Satisfied 1349 Content 628 Sentiment Analysis Mining Facebook for Feelings Angry Angry Pissed 3851 Annoyed Frustrated 1145 Disappointed 2534 Disgusted 1566 Fearful Worried 3274 Scared 2075 Anxious 1002 Shocked 1391 Confused 3904 Empowered Determined Confident 2341

31 The Revolution Learning from : Text, Feelings and Machine Learning Classifier Sentiment Analysis Mining Facebook for Feelings Basic Feelings (5-way classification) Classifier: MaxEnt Training Accuracy:.87 Testing Accuracy:.75 (10-fold validation) Arousal (2-way classification) Classifier: MaxEnt Training Accuracy:.99 Testing Accuracy:.80 (10-fold validation) Valence (2-way classification) Classifier: MaxEnt Training Accuracy:.99 Testing Accuracy:.83 (10-fold validation)

32 The Revolution Learning from : Text, Feelings and Machine Learning Sentiment Analysis Mining Facebook for Feelings Two-D Classification: Valence and Arousal

33 The Revolution Learning from : Text, Feelings and Machine Learning Sentiment Analysis Mining Facebook for Feelings Two-D Classification: Comparisons

34 The Revolution Learning from : Text, Feelings and Machine Learning Sentiment Analysis Mining Facebook for Feelings Feeling Meter: Manual Assessment Test Set: 160 examples from different sources Manual Task: Order Feelings Expressed (1 is most expressed, 5 least; 0 not expressed at all) Results: Binary Decision is feeling expressed or not? (Ignore examples where 1st coder notes no feelings expressed leaves 92 examples) Agreement on Feelings Expressed 1st coder vs 2nd coder: out of 459 in 92 cases 1st coder vs System: out of 459 in 92 cases

35 The Revolution Learning from : Text, Feelings and Machine Learning Sentiment Analysis Mining Facebook for Feelings

36 The Revolution Learning from : Text, Feelings and Machine Learning Big Transportation and Trade Data Analytics at Roskilde Festival Assessment of Country Logistics Systems What are logistics and supply chain costs in different countries? Specific transportation system cost categories like road, rail, air etc. Interaction of these costs with each other and with information and communication systems Relevant to the investment decision-making considerations of firms

37 The Revolution Learning from : Text, Feelings and Machine Learning An Analysis based on Reports Big Transportation and Trade Data Analytics at Roskilde Festival

38 The Revolution Learning from : Text, Feelings and Machine Learning Big Transportation and Trade Data Analytics at Roskilde Festival Assessing Relevant Factors from Reports

39 The Revolution Learning from : Text, Feelings and Machine Learning Big Transportation and Trade Data Analytics at Roskilde Festival A New Analysis using Language Technology Extract relevant factors based on distribution of words and terms in reports Use metrics like TFIDF, which finds terms that are likely to be characteristic of a given text With automatic analysis, can consider 10 or 100 times larger quantities of data reports over a ten year period, with dozens of countries

40 The Revolution Learning from : Text, Feelings and Machine Learning Big Transportation and Trade Data Analytics at Roskilde Festival

41 The Revolution Learning from : Text, Feelings and Machine Learning Big Transportation and Trade Data Analytics at Roskilde Festival Roskilde slides from Per Østergaard Jacobsen (CBS) and Henrik Hammer Eliassen (IBM)

42 The Revolution Learning from : Text, Feelings and Machine Learning Big Transportation and Trade Data Analytics at Roskilde Festival

43 The Revolution Learning from : Text, Feelings and Machine Learning Big Transportation and Trade Data Analytics at Roskilde Festival

44 The Revolution Learning from : Text, Feelings and Machine Learning Big Transportation and Trade Data Analytics at Roskilde Festival

45 The Revolution Learning from : Text, Feelings and Machine Learning Big Transportation and Trade Data Analytics at Roskilde Festival

46 The Revolution Learning from : Text, Feelings and Machine Learning Analysis Big Transportation and Trade Data Analytics at Roskilde Festival Where do people go? What do they buy? Machine Learning and AI can predict: under a given set of conditions (weather, previous movements, age, gender, etc), what is the probability of a given purchase? Watson technology is being brought to bear on such questions Relevant for supply chain

47 The Revolution Learning from : Text, Feelings and Machine Learning All industries will be fundamentally transformed by Big Data Many changes in areas like transportation, consumer forecasting that are crucial for supply chain management Lots of things happening at CBS!

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