1. Data Sources, Use Cases and Capabilities
Text Mining:
Data Sources, Use Cases and Capabilities
Kayvis Damptey
Jie Zhang
Dec 8, 2016

2. What is Text Mining?

3. Text Mining
Text Mining uses documents to identify insightful patterns within the text. Thus allowing managers to summarize/organize huge collections of documents and automate detection based on useful linguistic patterns.
Detecting these patterns can be done via the following methods:
Keyword/Event Detection
Word Clouds Visualizations
Natural Language Processing
Topic Discovery/Category Assignment
Sentiment Analysis
Financial Management Services
November 13, 2018

4. What skills are needed to do Text Mining?
Understand Linguistic Nuances
Domain Knowledge
Probability and Linear Algebra
Computer Programming
Financial Management Services
November 13, 2018

5. Past Use Case: Business Problem: Remedy Topic Discovery
With many tickets coming into Financial Support Center, there was a need to understand what general topics were being discussed in many of the tickets to better understand which issue need to be resolved faster or had the most tickets.
Financial Management Services
November 13, 2018

6. Past Use Cases: Remedy Topic Discovery Approach: Data Cleansing:
Curate data for modeling, including relevant customer data, and keywords in the text, spell checking etc.
Filter data based on things like excessive symbols (and thus maybe just a long exchange)
Standardize slang/abbreviations and clean the text from spelling errors
Data Transformation:
By creating a “ticket by term” matrix the tickets can be scored based on their term-frequency combinations (which is an approximation of the antonyms and synonyms).
Modeling and Scoring:
The tickets are then grouped based on the proportion of the co-occurrence of their term-frequency combination score.
Financial Management Services
November 13, 2018

7. Past Use Cases: Remedy Topic Discovery from 1,168 tickets Solution:
Financial Management Services
November 13, 2018

8. Everyday Textmining

9. How might text mining be useful in everyday situations?
Automatic Categorization and response to incoming documents
Detect strong customer sentiment towards a product or service
Discovering Common Topics of many documents
Summarizing documents
Detecting comments about actions taken by key partners
Extract location information from descriptions
Financial Management Services
November 13, 2018

10. Jie Zhang (jzhang28@Stanford.edu) Kayvis Damptey (kayvis@Stanford.edu)