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| 2
Structured Vs Unstructured Data
Defined vs Undefined Data
Qualitative vs Quantitative Data
Storage in Data Houses vs Data Lakes
Easy vs Hard to Analyze
Predefined format vs a variety of formats
| 3
https://www.xplenty.com/blog/structured-vs-unstructured-data-key-differences/#:~:text=Structured%20data%20is%20clearly%20defined,stored%20in%20its%20native%20format.&text=Structured%20data%20exists%20in%20predefined,in%20a%20variety%20of%20formats.
Size of Unstructured
Data
| 4
Hurricane Sandy -
2012
• Hit New York and its suburbs, and the Long Island.
• At least 53 people died in New York as a result of the storm.
• Thousands of homes and an estimated 250,000 vehicles were
destroyed during the storm.
• As per the study from a team of academics at the University of
Buffalo, USA, 86 to 91% of users on Twitter were spreading
misinformation during these disasters, one was man-made and the
other was natural.
• As if it was not enough, after knowing the truth less than 20
percent users tried to put correct information and less than 10%
users deleted the wrong information.
• In another study by MIT, it concluded that any wrong information
has 70% more likelihood to be retweeted
| 5
Understanding People
Many companies maintain online presences
Managing public perception in age of instant
communication essential
Reacting to changing sentiment, identifying offensive
posts, determining topics of interest…
How can we use analytics to address this?
| 6
| 7
Challenges
Huge, e.g. whatsapp has 69 MM messages everyday
Unstructured
Even at a small scale, cost and time is a challenge
| 8
How can computers help?
Computers need to understand text
This field is called Natural Language Processing
The goal is to understand and derive meaning from human language
It == Bag?
It == Car??
| 9
“I put my bag in the car. It is large
and blue”
Metaphor
| 10
My brother is a devil
Homonyms
| 11
Sarcasm
| 12
https://www.gminsights.com/industry-
analysis/text-analytics-market
| 13
BAG OF WORDS
Frequency
| 14
| 15
Bag of words
| 16
Two
Documents
Irregularities – lower case or upper case
| 18
University university uNIVerSITY
University University University
University
3
Irregularities – punctuation
| 19
--University #University University!
University University University
University
3
Punctuation also
causes problems –
basic approach is
to remove
everything that
isn’t a,b,…,z
Removing Unhelpful Terms
Many words are frequently used but are only meaningful in a sentence - “stop
words”
Examples: the, is, at, which…
Unlikely to improve machine learning prediction quality
Remove to reduce size of data
| 20
Stemming
| 21
Do we need to
draw a
distinction
between the
following words?
Argue Argued Argues Arguing
Argu Argu Argu Argu
Argu
4
Article
Tweets
Feedback
Process
| 22
Unhelpful terms
Irregularities
Stemming
White spaces
Numbers
Frequency of words
Word cloud
Association words
Sentiment Analysis
• Positive, Negative
• Extent of Positive-Negative
• Feelings, like trust
Predictive Analytics

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Structured vs Unstructured Data Differences

  • 1. | 2
  • 2. Structured Vs Unstructured Data Defined vs Undefined Data Qualitative vs Quantitative Data Storage in Data Houses vs Data Lakes Easy vs Hard to Analyze Predefined format vs a variety of formats | 3 https://www.xplenty.com/blog/structured-vs-unstructured-data-key-differences/#:~:text=Structured%20data%20is%20clearly%20defined,stored%20in%20its%20native%20format.&text=Structured%20data%20exists%20in%20predefined,in%20a%20variety%20of%20formats.
  • 4. Hurricane Sandy - 2012 • Hit New York and its suburbs, and the Long Island. • At least 53 people died in New York as a result of the storm. • Thousands of homes and an estimated 250,000 vehicles were destroyed during the storm. • As per the study from a team of academics at the University of Buffalo, USA, 86 to 91% of users on Twitter were spreading misinformation during these disasters, one was man-made and the other was natural. • As if it was not enough, after knowing the truth less than 20 percent users tried to put correct information and less than 10% users deleted the wrong information. • In another study by MIT, it concluded that any wrong information has 70% more likelihood to be retweeted | 5
  • 5. Understanding People Many companies maintain online presences Managing public perception in age of instant communication essential Reacting to changing sentiment, identifying offensive posts, determining topics of interest… How can we use analytics to address this? | 6
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  • 7. Challenges Huge, e.g. whatsapp has 69 MM messages everyday Unstructured Even at a small scale, cost and time is a challenge | 8 How can computers help? Computers need to understand text This field is called Natural Language Processing The goal is to understand and derive meaning from human language
  • 8. It == Bag? It == Car?? | 9 “I put my bag in the car. It is large and blue”
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  • 17. Irregularities – lower case or upper case | 18 University university uNIVerSITY University University University University 3
  • 18. Irregularities – punctuation | 19 --University #University University! University University University University 3 Punctuation also causes problems – basic approach is to remove everything that isn’t a,b,…,z
  • 19. Removing Unhelpful Terms Many words are frequently used but are only meaningful in a sentence - “stop words” Examples: the, is, at, which… Unlikely to improve machine learning prediction quality Remove to reduce size of data | 20
  • 20. Stemming | 21 Do we need to draw a distinction between the following words? Argue Argued Argues Arguing Argu Argu Argu Argu Argu 4
  • 21. Article Tweets Feedback Process | 22 Unhelpful terms Irregularities Stemming White spaces Numbers Frequency of words Word cloud Association words Sentiment Analysis • Positive, Negative • Extent of Positive-Negative • Feelings, like trust Predictive Analytics