The document discusses techniques for transforming unstructured text into structured data, focusing on sentiment analysis, topic modeling, named entity recognition, and event extraction. It describes various methods used in these processes, including sentiment categorization, dominant theme identification, and entity categorization. Additionally, it outlines practical applications for these text mining techniques in areas such as brand monitoring, data exploration, and event extraction.

1. From Unstructured Text to
Structured Data
Dan Sullivan
MarTechPDX Meetup
February 21, 2018

2. “Text is unstructured”

3. Unstructured?

4. “Unstructured” to Structured
“We booked a Napa group
tour. Booking and pickup
was seamless. All very
professional. Rob our
driver was fantastic. He
has very deep roots in
Napa and is a wealth of
info.”

5. Identifying Structured Attributes of Text
● Sentiment Analysis
● Topic Modeling
● Named Entity Recognition
● Event Extraction

6. Sentiment Analysis
* Analysis of tone or opinion of a communication
* Polarity: {positive, neutral, negative}
* Categorization: {angry, pleased, confused …}
* Scale -10 … +10

7. Sentiment Analysis Techniques
● Keywords
● Affective Norms
for English Words
(ANEW)
Image Source: https://www.csc2.ncsu.edu/faculty/healey/tweet_viz/tweet_app/

8. Topic Modeling
● Technique for identify dominant themes in
document
● Does not require training
● Multiple Algorithms
○ Probabilistic Latent Semantic Indexing (PLSI)
○ Latent Dirichlet allocation (LDA)
● Assumptions
○ Documents about a mixture of topics
○ Words used in document attributable to topic

9. Hotels, Cambodia,
pop-up restaurant
Image source: NYTimes.com
Cuisine, small plates,
Las Vegas
Cuisine, vegetables,
Alaska,

10. Documents have Multiple Topics
● Topics represented by words; documents about a set of topics
○ Doc 1: 50% hotel, 50% beach
○ Doc 2: 25% cuisine, 30% Thailand, 45% vegitarian
○ Doc 3: 30% wine, 40% Napa, 30% roadway
● Learning Topics
○ Probability of topic given a document P(topic|doc)
○ Probability of word given a topic P(word|topic)
○ Reassign word to new topic with probability

11. Named Entity Recognition
● Processes of identifying words and
phrases describing objects in
specific categories
● Common classes of entities:
● Persons
● Organizations
● Geographic locations
● Dates
● Monetary amounts

12. Travel Review
"Space needle had beautiful views
of the city I will recommend it for
people of all ages, the Chihuahua
gardens were amazing an
unexpected bonus to my space
needle visit this are must see when
visiting Seattle“

13. Named Entity Recognition Techniques
● Linguistic - utilize structure of sentence
● Statistical – detect patterns in training
● Custom patterns – regular expressions
● Dictionaries

14. Event Extraction
● Entities and relations between entities
○ Company A acquires Company B
○ Engineer A filed patent application on Topic B on
Date C
○ Politician P announces A on Twitter on Date B
● Assign roles to entities
● Assign categories to entities

15. Biomedical Example

16. Methods and Purpose
Sentiment Analysis What is the tone of the text?
Topic Modeling What concepts are discussed?
Named Entity Recognition What entities are mentioned?
Event Extraction How are entities related?

17. Text Mining Tools

18. Q & A

19. Sentiment Analysis Use Cases
* Brand monitoring
* Competitive intelligence
* Demographic modeling
* Campaign analysis

20. Topic Model Use Cases
● Data exploration in large corpus
● Pre-classification analysis
● Identify dominant themes

21. Named Entity Recognition Use Cases
● Name normalization
● Entity correlation
● Quantified metrics based on texts
● Building block for event extraction

22. Background
● Worked in AI and natural
language processing prior to AI
Winter
● Most recent work in NLP involved
text mining biomedical literature
● Focus on how to map
“unstructured” text to data
structures that are amenable to
structured analytics