Computer-Based Text- and Data Analysis Technologies and Applications. Mark Cieliebak
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1 Computer-Based Text- and Data Analysis Technologies and Applications Mark Cieliebak
2 Data Scientist analyze Data Library use 2
3 About Me Mark Cieliebak + Software Engineer & Data Scientist + PhD in Computer Science + CIO at Netbreeze (now Microsoft) + >30 scientific publications Lecturer at CEO of SpinningBytes AG 3
4 "Classical" Data Analysis Uses IT to retrieve, store, search, count, calculate, compare, visualize Peptide Sequencing data. 4
5 Computer-Based Data Analytics Started with Artificial intelligence in the 1960's Analyzes huge amounts of data Uses Machine Learning for Pattern Detection Image and Text Classification Predictive Analysis Data Clustering and many more! 5
6 Applications of Data Analytics Internet Search IBM Watson (wins Jeopardy in 2011) Spelling Correction Deep Blue (beats Kasparow 1997) DATA ANALYTICS Voice Recognition Recommendation Systems Machine Translation 6 Selfdriving Cars Spam Detection
7 Foundation of Data Analytics Memory: 64'000 Bytes 8'589'934'592 Bytes Performance: 500'000 FLOPS 33'863'000'000'000'000 FLOPS Cost per GFLOP: (1984) $42'780' $0.08 7
8 Foundation of Data Analytics 8
9 Foundations of Data Analytics Deep Learning Faster Computers + More Data + Better Algorithms 9
10 Application Training Machine Learning in a Nutshell Predicted Label 10
11 Application: Social Media Analysis Sentiment Analysis Topic Extraction Trend Detection Alerting 11
12 Twitter Facts Data Access Free Access with Search API Commercial Access to Firehose Stream (all or 10%) 12
13 Sentiment Analysis on Twitter #StackOverflow names #Apple #Swift the world's most loved #programming language 13
14 Sentiment Analysis: Performance of Commercial Tools (F1-Score) 0,7 0,6 0,5 0,4 0,3 0,2 Average of All Tools Best Tool per Corpus Overall Best Tool (Sentigem): 0,1 0 Experimantal Setup: 9 commercial sentiment analysis tools evaluated on 7 public corpora with short texts. 14
15 Take-Home Lesson Sentiment Tools on short texts achieve on average an F1-Score of 51%. 15
16 Application: Newspaper Segmentation 16
17 Application: Sales Prediction 17
18 More Applications Expert Match Face/Image Recognition Foundation Register 18 Speaker Detection
19 What do Data Scientists Need? 19
20 Data Sources Experimental Data Reference Datasets Dictionaires, Ontologies, Thesaurus Scientific Papers Social Media Media Archives: Newspapers, Magazines Websites Live Streams: Twitter, Online News, Product Reviews Videos/Movies/Pictures Books: Belletristic, Technical Literature Wikipedia Hand-crafted Datasets etc. etc. 20
21 Data Provisioning Data Collections: Linguistic Data Consortium European Language Ressource Association NISt TIMIT etc. Access, Licensing Open Data: Open Governmental Data OGD Open Research Data ORD Linked Open Data Participation, Guidelines 21
22 Data Scientist analyze support Data Library use 22
23 Digitizing Make Information Accessible! Extract Text, Images, Charts, Tables Categorize Search Browse (Summarize) 23
24 Data Integration Combining data from different sources is very time consuming! 24
25 SODES: Automatic Data Integration Data enters SODES via Linking to, e.g., CKAN API Crawling User upload Semantic Context Comprehension Matching of columns Data Quality Improvements Export to various standard formats Enables analytics in specialized tools of choice Automatic Data Intake Search Preview Download Integration Content based search on Full text of data Full text of meta data Descriptions of data sets Easy data exploration enabled by All integrable search results in one table Statistical standard plots & measures 25
26 Talk in Short Text and Data Analytics is successful Researcher need access to data Data Scientists have powerful tools 26
27 Thank You! Mark Cieliebak Institute of Applied Information Technology (InIT) Winterthur, Switzerland Website: 27
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