What is involved in text mining
Find out what the related areas are that text mining connects with, associates with, correlates with or affects, and which require thought, deliberation, analysis, review and discussion. This unique checklist stands out in a sense that it is not per-se designed to give answers, but to engage the reader and lay out a text mining thinking-frame.
How far is your company on its text mining journey?
Take this short survey to gauge your organization’s progress toward text mining leadership. Learn your strongest and weakest areas, and what you can do now to create a strategy that delivers results.
To address the criteria in this checklist for your organization, extensive selected resources are provided for sources of further research and information.
Start the Checklist
Below you will find a quick checklist designed to help you think about which text mining related domains to cover and 183 essential critical questions to check off in that domain.
The following domains are covered:
text mining, text mining, Psychological profiling, Joint Information Systems Committee, Named entity recognition, Research Council, National Centre for Text Mining, Content analysis, Information retrieval, Structured data, Exploratory data analysis, Name resolution, Open access, Concept mining, Predictive analytics, Pattern recognition, Business rule, Part of speech tagging, Hargreaves review, Record linkage, Internet news, Copyright law of Japan, Corpus manager, Text Analysis Portal for Research, European Commission, National Security, Spam filter, Full text search, Google Book Search Settlement Agreement, News analytics, Text corpus, Text clustering, Fair use, Lexical analysis, Document summarization, Gender bias, Customer relationship management, Data mining, Information extraction, Ad serving, Business intelligence, Sentiment Analysis, Intelligence analyst, Noun phrase, Document processing, Web mining, Predictive classification, National Institutes of Health, PubMed Central, Information Awareness Office, Big data, Customer attrition, Biomedical text mining, Database Directive, Market sentiment, Commercial software:
text mining Critical Criteria:
Start text mining tactics and innovate what needs to be done with text mining.
– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about text mining. How do we gain traction?
– What are current text mining Paradigms?
– What are our text mining Processes?
text mining Critical Criteria:
Guide text mining planning and modify and define the unique characteristics of interactive text mining projects.
– What knowledge, skills and characteristics mark a good text mining project manager?
– Do the text mining decisions we make today help people and the planet tomorrow?
Psychological profiling Critical Criteria:
Analyze Psychological profiling risks and transcribe Psychological profiling as tomorrows backbone for success.
– What is the source of the strategies for text mining strengthening and reform?
– How is the value delivered by text mining being measured?
– Are there text mining problems defined?
Joint Information Systems Committee Critical Criteria:
Give examples of Joint Information Systems Committee adoptions and cater for concise Joint Information Systems Committee education.
– Think about the functions involved in your text mining project. what processes flow from these functions?
– Is there a text mining Communication plan covering who needs to get what information when?
– How can the value of text mining be defined?
Named entity recognition Critical Criteria:
Confer re Named entity recognition management and create a map for yourself.
– A compounding model resolution with available relevant data can often provide insight towards a solution methodology; which text mining models, tools and techniques are necessary?
– Think of your text mining project. what are the main functions?
– Who sets the text mining standards?
Research Council Critical Criteria:
Chart Research Council goals and don’t overlook the obvious.
– At what point will vulnerability assessments be performed once text mining is put into production (e.g., ongoing Risk Management after implementation)?
– In what ways are text mining vendors and us interacting to ensure safe and effective use?
National Centre for Text Mining Critical Criteria:
Detail National Centre for Text Mining management and get out your magnifying glass.
– What are our best practices for minimizing text mining project risk, while demonstrating incremental value and quick wins throughout the text mining project lifecycle?
– Does text mining analysis show the relationships among important text mining factors?
– Who will provide the final approval of text mining deliverables?
Content analysis Critical Criteria:
Think about Content analysis adoptions and improve Content analysis service perception.
– How will we insure seamless interoperability of text mining moving forward?
– How will you know that the text mining project has been successful?
– What are internal and external text mining relations?
Information retrieval Critical Criteria:
Mine Information retrieval governance and look in other fields.
– What potential environmental factors impact the text mining effort?
– Do you monitor the effectiveness of your text mining activities?
Structured data Critical Criteria:
Trace Structured data leadership and question.
– What tools do you consider particularly important to handle unstructured data expressed in (a) natural language(s)?
– Does your organization have the right tools to handle unstructured data expressed in (a) natural language(s)?
– How do we make it meaningful in connecting text mining with what users do day-to-day?
– Should you use a hierarchy or would a more structured database-model work best?
– What are the Key enablers to make this text mining move?
Exploratory data analysis Critical Criteria:
Conceptualize Exploratory data analysis planning and don’t overlook the obvious.
– Is there any existing text mining governance structure?
Name resolution Critical Criteria:
Have a session on Name resolution leadership and suggest using storytelling to create more compelling Name resolution projects.
– Do several people in different organizational units assist with the text mining process?
– How do we manage text mining Knowledge Management (KM)?
– What is Effective text mining?
Open access Critical Criteria:
Ventilate your thoughts about Open access decisions and revise understanding of Open access architectures.
– Among the text mining product and service cost to be estimated, which is considered hardest to estimate?
– Do we have past text mining Successes?
Concept mining Critical Criteria:
Pay attention to Concept mining issues and point out improvements in Concept mining.
– For your text mining project, identify and describe the business environment. is there more than one layer to the business environment?
– Do we monitor the text mining decisions made and fine tune them as they evolve?
Predictive analytics Critical Criteria:
Grade Predictive analytics risks and display thorough understanding of the Predictive analytics process.
– What are the disruptive text mining technologies that enable our organization to radically change our business processes?
– What are direct examples that show predictive analytics to be highly reliable?
– How do we go about Comparing text mining approaches/solutions?
– Who needs to know about text mining ?
Pattern recognition Critical Criteria:
Audit Pattern recognition risks and report on setting up Pattern recognition without losing ground.
Business rule Critical Criteria:
Explore Business rule tasks and plan concise Business rule education.
– If enterprise data were always kept fully normalized and updated for business rule changes, would any system re-writes or replacement purchases be necessary?
– Where do ideas that reach policy makers and planners as proposals for text mining strengthening and reform actually originate?
– Is text mining dependent on the successful delivery of a current project?
Part of speech tagging Critical Criteria:
Deliberate Part of speech tagging tactics and catalog what business benefits will Part of speech tagging goals deliver if achieved.
– How do we measure improved text mining service perception, and satisfaction?
– What is our formula for success in text mining ?
Hargreaves review Critical Criteria:
Accelerate Hargreaves review engagements and oversee implementation of Hargreaves review.
– What management system can we use to leverage the text mining experience, ideas, and concerns of the people closest to the work to be done?
– How can we incorporate support to ensure safe and effective use of text mining into the services that we provide?
– What are the Essentials of Internal text mining Management?
Record linkage Critical Criteria:
Investigate Record linkage management and acquire concise Record linkage education.
– What are your current levels and trends in key measures or indicators of text mining product and process performance that are important to and directly serve your customers? how do these results compare with the performance of your competitors and other organizations with similar offerings?
– Meeting the challenge: are missed text mining opportunities costing us money?
– Why is text mining important for you now?
Internet news Critical Criteria:
Facilitate Internet news issues and inform on and uncover unspoken needs and breakthrough Internet news results.
– How much does text mining help?
Copyright law of Japan Critical Criteria:
Closely inspect Copyright law of Japan results and adjust implementation of Copyright law of Japan.
– How does the organization define, manage, and improve its text mining processes?
– Who will be responsible for documenting the text mining requirements in detail?
– What are all of our text mining domains and what do they do?
Corpus manager Critical Criteria:
Consider Corpus manager decisions and arbitrate Corpus manager techniques that enhance teamwork and productivity.
– How do mission and objectives affect the text mining processes of our organization?
Text Analysis Portal for Research Critical Criteria:
Have a round table over Text Analysis Portal for Research tactics and find the ideas you already have.
– What is the purpose of text mining in relation to the mission?
– Can Management personnel recognize the monetary benefit of text mining?
European Commission Critical Criteria:
Confer over European Commission risks and adjust implementation of European Commission.
– To what extent does management recognize text mining as a tool to increase the results?
– Is the text mining organization completing tasks effectively and efficiently?
National Security Critical Criteria:
Illustrate National Security leadership and stake your claim.
Spam filter Critical Criteria:
Collaborate on Spam filter governance and handle a jump-start course to Spam filter.
– In the case of a text mining project, the criteria for the audit derive from implementation objectives. an audit of a text mining project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any text mining project is implemented as planned, and is it working?
– How important is text mining to the user organizations mission?
Full text search Critical Criteria:
Win new insights about Full text search risks and find answers.
– what is the best design framework for text mining organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?
– What is the total cost related to deploying text mining, including any consulting or professional services?
Google Book Search Settlement Agreement Critical Criteria:
Weigh in on Google Book Search Settlement Agreement management and correct Google Book Search Settlement Agreement management by competencies.
– What tools do you use once you have decided on a text mining strategy and more importantly how do you choose?
– Who will be responsible for deciding whether text mining goes ahead or not after the initial investigations?
News analytics Critical Criteria:
Accommodate News analytics issues and report on the economics of relationships managing News analytics and constraints.
– How do we go about Securing text mining?
– What threat is text mining addressing?
Text corpus Critical Criteria:
Start Text corpus quality and sort Text corpus activities.
– What prevents me from making the changes I know will make me a more effective text mining leader?
– Who are the people involved in developing and implementing text mining?
Text clustering Critical Criteria:
Discuss Text clustering planning and catalog what business benefits will Text clustering goals deliver if achieved.
– How can you negotiate text mining successfully with a stubborn boss, an irate client, or a deceitful coworker?
– Who is the main stakeholder, with ultimate responsibility for driving text mining forward?
Fair use Critical Criteria:
Powwow over Fair use tactics and know what your objective is.
– What vendors make products that address the text mining needs?
– How can you measure text mining in a systematic way?
Lexical analysis Critical Criteria:
Shape Lexical analysis adoptions and probe Lexical analysis strategic alliances.
Document summarization Critical Criteria:
Guard Document summarization outcomes and report on the economics of relationships managing Document summarization and constraints.
– What are the long-term text mining goals?
Gender bias Critical Criteria:
Face Gender bias governance and explain and analyze the challenges of Gender bias.
– Are we Assessing text mining and Risk?
Customer relationship management Critical Criteria:
Analyze Customer relationship management planning and test out new things.
– Can visitors/customers easily find all relevant information about your products (e.g., prices, options, technical specifications, quantities, shipping information, order status) on your website?
– Is a significant amount of your time taken up communicating with existing clients to resolve issues they are having?
– Support – how can we drive support for using the escalation processes for service, support and billing issues?
– How do you enhance existing cache management techniques for context-dependent data?
– Can you make product suggestions based on the customers order or purchase history?
– What is the best way to integrate social media into existing CRM strategies?
– Is there a pattern to our clients buying habits (e.g., seasonal)?
– Do you have a mechanism to collect visitor/customer information?
– Which business environmental factors did lead to our use of CRM?
– what is Different Between B2C B2B Customer Experience Management?
– Can you identify your customers when they visit your website?
– What are the key application components of our CRM system?
– Are the offline synchronization subscriptions valid?
– What type of information may be released to whom?
– Is there an IVR abandon rate; if so, what is it?
– What Type of Information May be Released?
– Is the metadata cache size acceptable?
– What happens to customizations?
– Who are my customers?
Data mining Critical Criteria:
Value Data mining quality and shift your focus.
– Do you see the need to clarify copyright aspects of the data-driven innovation (e.g. with respect to technologies such as text and data mining)?
– What types of transactional activities and data mining are being used and where do we see the greatest potential benefits?
– What is the difference between Data Analytics Data Analysis Data Mining and Data Science?
– What is the difference between business intelligence business analytics and data mining?
– Is business intelligence set to play a key role in the future of Human Resources?
– Why is it important to have senior management support for a text mining project?
– What programs do we have to teach data mining?
Information extraction Critical Criteria:
Deliberate over Information extraction quality and find out.
– Risk factors: what are the characteristics of text mining that make it risky?
Ad serving Critical Criteria:
Analyze Ad serving visions and gather practices for scaling Ad serving.
– What are our needs in relation to text mining skills, labor, equipment, and markets?
– What tools and technologies are needed for a custom text mining project?
Business intelligence Critical Criteria:
Be clear about Business intelligence goals and point out Business intelligence tensions in leadership.
– Does the software let users work with the existing data infrastructure already in place, freeing your IT team from creating more cubes, universes, and standalone marts?
– Can your software connect to all forms of data, from text and Excel files to cloud and enterprise-grade databases, with a few clicks?
– what is the most effective tool for Statistical Analysis Business Analytics and Business Intelligence?
– What is the difference between a data scientist and a business intelligence analyst?
– Does your software facilitate the setting of thresholds and provide alerts to users?
– What documentation is provided with the software / system and in what format?
– Does creating or modifying reports or dashboards require a reporting team?
– what is the difference between Data analytics and Business Analytics If Any?
– Does your BI solution require weeks or months to deploy or change?
– Number of data sources that can be simultaneously accessed?
– What business intelligence systems are available?
– Will your product work from a mobile device?
– What are typical data-mining applications?
– What is required to present video images?
– What is your expect product life cycle?
Sentiment Analysis Critical Criteria:
Meet over Sentiment Analysis results and define what our big hairy audacious Sentiment Analysis goal is.
– How representative is twitter sentiment analysis relative to our customer base?
– Are there recognized text mining problems?
Intelligence analyst Critical Criteria:
Detail Intelligence analyst governance and improve Intelligence analyst service perception.
– Do we aggressively reward and promote the people who have the biggest impact on creating excellent text mining services/products?
– What are the key skills a Business Intelligence Analyst should have?
Noun phrase Critical Criteria:
Powwow over Noun phrase adoptions and reduce Noun phrase costs.
– Why are text mining skills important?
Document processing Critical Criteria:
Check Document processing tactics and test out new things.
– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to text mining?
– Have the types of risks that may impact text mining been identified and analyzed?
Web mining Critical Criteria:
Merge Web mining leadership and attract Web mining skills.
– How can we improve text mining?
Predictive classification Critical Criteria:
Value Predictive classification decisions and do something to it.
– Think about the people you identified for your text mining project and the project responsibilities you would assign to them. what kind of training do you think they would need to perform these responsibilities effectively?
National Institutes of Health Critical Criteria:
Talk about National Institutes of Health goals and gather National Institutes of Health models .
– How do we keep improving text mining?
PubMed Central Critical Criteria:
Differentiate PubMed Central leadership and look in other fields.
– What are your results for key measures or indicators of the accomplishment of your text mining strategy and action plans, including building and strengthening core competencies?
– How do we know that any text mining analysis is complete and comprehensive?
Information Awareness Office Critical Criteria:
Scan Information Awareness Office projects and explain and analyze the challenges of Information Awareness Office.
– What are the record-keeping requirements of text mining activities?
Big data Critical Criteria:
Incorporate Big data leadership and triple focus on important concepts of Big data relationship management.
– While a move from Oracles MySQL may be necessary because of its inability to handle key big data use cases, why should that move involve a switch to Apache Cassandra and DataStax Enterprise?
– What are the main obstacles that prevent you from having access to all the datasets that are relevant for your organization?
– To what extent does your organization have experience with big data and data-driven innovation (DDI)?
– How should we organize to capture the benefit of Big Data and move swiftly to higher maturity stages?
– Does big data threaten the traditional data warehouse business intelligence model stack?
– What type(s) of data does your organization find relevant but has not yet been able to exploit?
– Is senior management in your organization involved in big data-related projects?
– What is the right technique for distributing domains across processors?
– Does your organization have a strategy on big data or data analytics?
– Do you see a need to share data processing facilities?
– Even when we have a lot of data, do we understand it?
– More efficient all-to-all operations (similarities)?
– Can analyses improve with more data to process?
– How much data might be lost to pruning?
– What preprocessing do we need to do?
– Does Big Data Really Need HPC?
– How to deal with ambiguity?
– Find traffic bottlenecks ?
– How much data so far?
Customer attrition Critical Criteria:
Refer to Customer attrition leadership and transcribe Customer attrition as tomorrows backbone for success.
Biomedical text mining Critical Criteria:
Shape Biomedical text mining planning and stake your claim.
– How do your measurements capture actionable text mining information for use in exceeding your customers expectations and securing your customers engagement?
Database Directive Critical Criteria:
Experiment with Database Directive tasks and know what your objective is.
– What are the barriers to increased text mining production?
Market sentiment Critical Criteria:
Define Market sentiment results and slay a dragon.
– What are your key performance measures or indicators and in-process measures for the control and improvement of your text mining processes?
– Does the text mining task fit the clients priorities?
Commercial software Critical Criteria:
Be clear about Commercial software quality and get out your magnifying glass.
– What other jobs or tasks affect the performance of the steps in the text mining process?
This quick readiness checklist is a selected resource to help you move forward. Learn more about how to achieve comprehensive insights with the text mining Self Assessment:
Author: Gerard Blokdijk
CEO at The Art of Service | theartofservice.com
Gerard is the CEO at The Art of Service. He has been providing information technology insights, talks, tools and products to organizations in a wide range of industries for over 25 years. Gerard is a widely recognized and respected information expert. Gerard founded The Art of Service consulting business in 2000. Gerard has authored numerous published books to date.
To address the criteria in this checklist, these selected resources are provided for sources of further research and information:
text mining External links:
Named entity recognition External links:
NAMED ENTITY RECOGNITION – Microsoft Corporation
Create an OpenNLP model for Named Entity Recognition …
Research Council External links:
Welding Research Council – Menu
Family Research Council – SourceWatch
Family Research Council Corporate Portal
National Centre for Text Mining External links:
www.Nactem.ac.uk – National Centre for Text Mining — Text
John McNaught | National Centre for Text Mining | …
National Centre for Text Mining – Revolvy
topics.revolvy.com/topic/National Centre for Text Mining
Content analysis External links:
Content analysis: Introduction – UC Davis, Psychology
Content Analysis | Pew Research Center
How to Do Content Analysis | The Classroom | Synonym
Information retrieval External links:
PPIRS – Past Performance Information Retrieval System
Information Retrieval – RMIT University
Introduction to Information Retrieval
Structured data External links:
n4e Ltd Structured Data cabling | Electrical Installations
Introduction to Structured Data | Search | Google Developers
Introduction to Structured Data | Search | Google Developers
Exploratory data analysis External links:
Exploratory Data Analysis with R | Pluralsight
Exploratory Data Analysis with R – Leanpub
Exploratory Data Analysis With R – Online Course | Udacity
Name resolution External links:
Microsoft TCP/IP Host Name Resolution Order
How to troubleshoot DNS name resolution on the Internet …
Open access External links:
Virtual Open Access Lab
JSciMed Central – Bringing Excellence in Open Access
SPARC: Advancing Open Access, Open Data, Open …
Concept mining External links:
Concept Mining using Conceptual Ontological Graph …
Predictive analytics External links:
Customer Analytics & Predictive Analytics Tools for Business
Predictive Analytics for Healthcare | Forecast Health
Predictive Analytics Software, Social Listening | NewBrand
Pattern recognition External links:
Pattern recognition (Computer file, 2006) [WorldCat.org]
Pattern Recognition – IMDb
Pattern Recognition — Alexander Whitley
Business rule External links:
Business Rules vs. Business Requirements …
[PDF]Business Rule Number – Internal Revenue Service
Part of speech tagging External links:
[PDF]Part of Speech Tagging – BGU
Record linkage External links:
Record linkage (eBook, 1946) [WorldCat.org]
Record linkage – WIREs Computational Statistics
wires.wiley.com › … › Vol 2 Issue 5 (September/October 2010)
“Record Linkage” by Stasha Ann Bown Larsen
Internet news External links:
Comprehension and Recall of Internet News: A …
Technology News – New Technology, Internet News, …
Copyright law of Japan External links:
Copyright Law of Japan | e-Asia
Corpus manager External links:
Virtual Corpus Manager – Archive of Department of …
Text Analysis Portal for Research External links:
tapor.ca – TAPoR – Text Analysis Portal for Research
tapor.ca – TAPoR – Text Analysis Portal for Research
www.tapor.ca TAPoR – Text Analysis Portal for Research
European Commission External links:
European Commission Code of Conduct for Data Centre …
European commission | World | The Guardian
National Security External links:
Premier Security Guard Services | Champion National Security
Home | CFNS | Citizens for National Security
National Security Agency for Intelligence Careers
Spam filter External links:
Configure your spam filter policies: Exchange Online Help
Spamhaus Technology – Email Spam filter services from Spamhaus
Daystarr Spam Filter :: Login
Full text search External links:
FDIC: Full Text Search
Google Book Search Settlement Agreement External links:
Google Book Search Settlement Agreement – Revolvy
www.revolvy.com/topic/Google Book Search Settlement Agreement
Text corpus External links:
ERIC – A Text Corpus Approach to an Analysis of the …
In linguistics, a corpus (plural corpora) or text corpus is a large and structured set of texts (now usually electronically stored and processed).
Text Corpus (www.narcis.nl)
Fair use External links:
Fair Use | Definition of Fair Use by Merriam-Webster
About the Fair Use Index | U.S. Copyright Office
Cases Archive – Stanford Copyright and Fair Use Center
Lexical analysis External links:
Lexical Analysis | The MIT Press
Gender bias External links:
Free gender bias Essays and Papers – 123HelpMe
Most Popular “Gender Bias” Titles – IMDb
Title IX and Gender Bias in Language – CourseBB
Customer relationship management External links:
Customer Relationship Management Login – NOVAtime
Salesnet CRM Solutions | Customer Relationship Management
Agile CRM – Customer Relationship Management
Data mining External links:
[USC04] 42 USC 2000ee-3: Federal agency data mining reporting
[PDF]Project Title: Data Mining to Improve Water Management
[PDF]Data Mining Mining Text Data – tutorialspoint.com
Information extraction External links:
[PDF]Title: Information Extraction from Muon …
[PDF]Information Extraction – CS 452 HOMEPAGE
Information Extraction — NYU Scholars
Ad serving External links:
ZEDO Ad Serving : Login
We do ad serving software right | OrbitSoft
Powerful Ad Serving Simplified – AdButler
Business intelligence External links:
[PDF]Position Title: Business Intelligence Analyst – ttra
Mortgage Business Intelligence Software :: Motivity Solutions
Benchmarking Digital Performance | Business Intelligence …
Sentiment Analysis External links:
YUKKA Lab – Sentiment Analysis
SearchBlox – Enterprise Search, Sentiment Analysis, …
Repustate – Sentiment analysis, social media sentiment …
Intelligence analyst External links:
What does an Intelligence Analyst do? – Sokanu
So you want to be an intelligence analyst | Matthew Burton
Noun phrase External links:
Grammar Bytes! :: The Noun Phrase
Browse and Read Noun Phrase Noun Phrase noun phrase
Document processing External links:
PRELIMINARY SYLLABUS FOR DOCUMENT PROCESSING
Document Outsourcing | Document Processing | Novitex
LINGO – Web Based EDI Document Processing
Web mining External links:
CSE 258 – Recommender Sys&Web Mining – UC San Diego
Intro to Web Mining – Scale Unlimited
Web mining | VCU Across the Spectrum
National Institutes of Health External links:
[PDF]NATIONAL INSTITUTES OF HEALTH – Clinical Center …
[PDF]NATIONAL INSTITUTES OF HEALTH
[PDF]NATIONAL INSTITUTES OF HEALTH
PubMed Central External links:
Need Images? Try PubMed Central | HSLS Update
TMC Library | PubMed Central
PubMed Central | Rutgers University Libraries
Information Awareness Office External links:
Information Awareness Office – SourceWatch
Big data External links:
Take 5 Media Group – Build an audience using big data
Pepperdata: DevOps for Big Data
Qognify: Big Data Solutions for Physical Security & …
Customer attrition External links:
What is customer attrition? | BigCommerce
Listening to Feedback Is How You Fight Customer Attrition
Biomedical text mining External links:
Biomedical Text Mining Applied To Document Retrieval …
[PDF]A Survey of Current Work in Biomedical Text Mining
What is Biomedical text mining? – Quora
Database Directive External links:
Overview: European Union Database Directive
European Union Database Directive – cyber.harvard.edu
Market sentiment External links:
Trade Followers – Stock Market Sentiment
Earnings Whispers Market Sentiment
Commercial software External links:
efile with Commercial Software | Internal Revenue Service
Commercial Software Errors | Department of Taxes