Top 168 text mining Criteria for Ready Action

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 168 essential critical questions to check off in that domain.

The following domains are covered:

text mining, Hargreaves review, Database Directive, Information visualization, text mining, Corpus manager, Psychological profiling, Customer attrition, Predictive classification, Research Council, Semantic web, Document summarization, Tribune Company, Concept mining, Ronen Feldman, Social sciences, Document processing, Information extraction, Competitive Intelligence, News analytics, Open source, Text categorization, Part of speech tagging, National Diet Library, Predictive analytics, Content analysis, Ad serving, Sequential pattern mining, Record linkage, Google Book Search Settlement Agreement, Pattern recognition, Exploratory data analysis, Spam filter, Name resolution, Information Awareness Office, Business intelligence, Sentiment Analysis, Business rule, Gender bias, Copyright Directive, National Centre for Text Mining, Full text search, Named entity recognition, European Commission, Fair use, Machine learning, Structured data, Plain text, Internet news, Commercial software, Text Analysis Portal for Research, Lexical analysis, Biomedical text mining, Social media, Copyright law of Japan:

text mining Critical Criteria:

Chat re text mining management and catalog what business benefits will text mining goals deliver if achieved.

– Risk factors: what are the characteristics of text mining that make it risky?

– How do we measure improved text mining service perception, and satisfaction?

– Have the types of risks that may impact text mining been identified and analyzed?

Hargreaves review Critical Criteria:

Conceptualize Hargreaves review visions and improve Hargreaves review service perception.

– Is there a text mining Communication plan covering who needs to get what information when?

– What are internal and external text mining relations?

– How do we Lead with text mining in Mind?

Database Directive Critical Criteria:

Discourse Database Directive adoptions and describe which business rules are needed as Database Directive interface.

– Do we cover the five essential competencies-Communication, Collaboration,Innovation, Adaptability, and Leadership that improve an organizations ability to leverage the new text mining in a volatile global economy?

– Who will provide the final approval of text mining deliverables?

– How can you measure text mining in a systematic way?

Information visualization Critical Criteria:

Conceptualize Information visualization failures and devise Information visualization key steps.

– How do you incorporate cycle time, productivity, cost control, and other efficiency and effectiveness factors into these text mining processes?

– How important is text mining to the user organizations mission?

text mining Critical Criteria:

Air ideas re text mining strategies and pay attention to the small things.

– What role does communication play in the success or failure of a text mining project?

– Does text mining analysis show the relationships among important text mining factors?

– How do we know that any text mining analysis is complete and comprehensive?

Corpus manager Critical Criteria:

Study Corpus manager governance and devise Corpus manager key steps.

– Are there any easy-to-implement alternatives to text mining? Sometimes other solutions are available that do not require the cost implications of a full-blown project?

– What vendors make products that address the text mining needs?

– How can the value of text mining be defined?

Psychological profiling Critical Criteria:

Adapt Psychological profiling tasks and create a map for yourself.

– How do we ensure that implementations of text mining products are done in a way that ensures safety?

– Are accountability and ownership for text mining clearly defined?

Customer attrition Critical Criteria:

Rank Customer attrition decisions and handle a jump-start course to Customer attrition.

– What sources do you use to gather information for a text mining study?

– How do we manage text mining Knowledge Management (KM)?

Predictive classification Critical Criteria:

Use past Predictive classification leadership and look at it backwards.

– For your text mining project, identify and describe the business environment. is there more than one layer to the business environment?

– What are the success criteria that will indicate that text mining objectives have been met and the benefits delivered?

– In a project to restructure text mining outcomes, which stakeholders would you involve?

Research Council Critical Criteria:

Accumulate Research Council tasks and be persistent.

– Consider your own text mining project. what types of organizational problems do you think might be causing or affecting your problem, based on the work done so far?

– What is the source of the strategies for text mining strengthening and reform?

– How would one define text mining leadership?

Semantic web Critical Criteria:

Analyze Semantic web issues and correct Semantic web management by competencies.

– 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 do we Identify specific text mining investment and emerging trends?

– Is text mining Required?

Document summarization Critical Criteria:

Talk about Document summarization risks and finalize specific methods for Document summarization acceptance.

– What will be the consequences to the business (financial, reputation etc) if text mining does not go ahead or fails to deliver the objectives?

– Why is it important to have senior management support for a text mining project?

– Are we making progress? and are we making progress as text mining leaders?

Tribune Company Critical Criteria:

Air ideas re Tribune Company quality and create Tribune Company explanations for all managers.

– Do several people in different organizational units assist with the text mining process?

– What are the usability implications of text mining actions?

– Do we all define text mining in the same way?

Concept mining Critical Criteria:

Facilitate Concept mining strategies and oversee Concept mining requirements.

– What prevents me from making the changes I know will make me a more effective text mining leader?

– Does text mining create potential expectations in other areas that need to be recognized and considered?

Ronen Feldman Critical Criteria:

Transcribe Ronen Feldman decisions and intervene in Ronen Feldman processes and leadership.

– 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?

– Who are the people involved in developing and implementing text mining?

Social sciences Critical Criteria:

Communicate about Social sciences leadership and report on the economics of relationships managing Social sciences and constraints.

– When a text mining manager recognizes a problem, what options are available?

Document processing Critical Criteria:

Rank Document processing quality and secure Document processing creativity.

– What tools and technologies are needed for a custom text mining project?

– Is there any existing text mining governance structure?

Information extraction Critical Criteria:

Probe Information extraction visions and get going.

– Think of your text mining project. what are the main functions?

– Is Supporting text mining documentation required?

Competitive Intelligence Critical Criteria:

Model after Competitive Intelligence results and forecast involvement of future Competitive Intelligence projects in development.

– Who will be responsible for making the decisions to include or exclude requested changes once text mining is underway?

– Do the text mining decisions we make today help people and the planet tomorrow?

News analytics Critical Criteria:

Examine News analytics governance and customize techniques for implementing News analytics controls.

– Does text mining analysis isolate the fundamental causes of problems?

– Will text mining deliverables need to be tested and, if so, by whom?

– Who needs to know about text mining ?

Open source Critical Criteria:

Participate in Open source issues and create a map for yourself.

– Is there any open source personal cloud software which provides privacy and ease of use 1 click app installs cross platform html5?

– How much do political issues impact on the decision in open source projects and how does this ultimately impact on innovation?

– What are the different RDBMS (commercial and open source) options available in the cloud today?

– Is open source software development faster, better, and cheaper than software engineering?

– Is maximizing text mining protection the same as minimizing text mining loss?

– Vetter, Infectious Open Source Software: Spreading Incentives or Promoting Resistance?

– What are some good open source projects for the internet of things?

– What are the best open source solutions for data loss prevention?

– Do you monitor the effectiveness of your text mining activities?

– Is open source software development essentially an agile method?

– What can a cms do for an open source project?

– Is there an open source alternative to adobe captivate?

– What are the open source alternatives to Moodle?

Text categorization Critical Criteria:

Group Text categorization adoptions and find out.

– Can Management personnel recognize the monetary benefit of text mining?

– How can skill-level changes improve text mining?

Part of speech tagging Critical Criteria:

Meet over Part of speech tagging failures and cater for concise Part of speech tagging education.

– Does the text mining task fit the clients priorities?

– Are there text mining problems defined?

National Diet Library Critical Criteria:

Read up on National Diet Library failures and work towards be a leading National Diet Library expert.

Predictive analytics Critical Criteria:

Collaborate on Predictive analytics strategies and forecast involvement of future Predictive analytics projects in development.

– In what ways are text mining vendors and us interacting to ensure safe and effective use?

– What are direct examples that show predictive analytics to be highly reliable?

– How much does text mining help?

Content analysis Critical Criteria:

Rank Content analysis planning and attract Content analysis skills.

– Where do ideas that reach policy makers and planners as proposals for text mining strengthening and reform actually originate?

– Do we monitor the text mining decisions made and fine tune them as they evolve?

Ad serving Critical Criteria:

Map Ad serving goals and get out your magnifying glass.

– Can we do text mining without complex (expensive) analysis?

Sequential pattern mining Critical Criteria:

Model after Sequential pattern mining adoptions and intervene in Sequential pattern mining processes and leadership.

– What are our needs in relation to text mining skills, labor, equipment, and markets?

– What are the barriers to increased text mining production?

– What are the business goals text mining is aiming to achieve?

Record linkage Critical Criteria:

Troubleshoot Record linkage engagements and cater for concise Record linkage education.

– What are your key performance measures or indicators and in-process measures for the control and improvement of your text mining processes?

– Meeting the challenge: are missed text mining opportunities costing us money?

– What are current text mining Paradigms?

Google Book Search Settlement Agreement Critical Criteria:

Debate over Google Book Search Settlement Agreement adoptions and probe the present value of growth of Google Book Search Settlement Agreement.

– Is the text mining organization completing tasks effectively and efficiently?

– How will we insure seamless interoperability of text mining moving forward?

Pattern recognition Critical Criteria:

Scan Pattern recognition issues and check on ways to get started with Pattern recognition.

– 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?

Exploratory data analysis Critical Criteria:

Disseminate Exploratory data analysis quality and work towards be a leading Exploratory data analysis expert.

– How do we keep improving text mining?

Spam filter Critical Criteria:

Shape Spam filter tasks and figure out ways to motivate other Spam filter users.

– Are we Assessing text mining and Risk?

– Why are text mining skills important?

Name resolution Critical Criteria:

Survey Name resolution risks and integrate design thinking in Name resolution innovation.

– How can you negotiate text mining successfully with a stubborn boss, an irate client, or a deceitful coworker?

– Which text mining goals are the most important?

– Why is text mining important for you now?

Information Awareness Office Critical Criteria:

Rank Information Awareness Office issues and modify and define the unique characteristics of interactive Information Awareness Office projects.

– Who is responsible for ensuring appropriate resources (time, people and money) are allocated to text mining?

Business intelligence Critical Criteria:

Prioritize Business intelligence issues and look at it backwards.

– Does your mobile solution allow you to interact with desktop-authored dashboards using touchscreen gestures like taps, flicks, and pinches?

– What information can be provided in regards to a sites usage and business intelligence usage within the intranet environment?

– Can you easily add users and features to quickly scale and customize to your organizations specific needs?

– How does Tableau stack up against the traditional BI software like Microstrategy or Business Objects?

– What is the future scope for combination of Business Intelligence and Cloud Computing?

– what is the BI software application landscape going to look like in the next 5 years?

– What is the difference between business intelligence and business analytics?

– Does your client support bi-directional functionality with mapping?

– Is Data Warehouseing necessary for a business intelligence service?

– What types of courses do you run and what are their durations?

– What else does the data tell us that we never thought to ask?

– What are the most efficient ways to create the models?

– What are the best client side analytics tools today?

– Will your product work from a mobile device?

– What level of training would you recommend?

– Make or buy BI Business Intelligence?

– Do you still need a data warehouse?

– Does your system provide APIs?

– How are you going to manage?

– Using dashboard functions?

Sentiment Analysis Critical Criteria:

Sort Sentiment Analysis results and finalize the present value of growth of Sentiment Analysis.

– Can we add value to the current text mining decision-making process (largely qualitative) by incorporating uncertainty modeling (more quantitative)?

– How representative is twitter sentiment analysis relative to our customer base?

Business rule Critical Criteria:

Have a round table over Business rule governance and budget for Business rule challenges.

– If enterprise data were always kept fully normalized and updated for business rule changes, would any system re-writes or replacement purchases be necessary?

– Who will be responsible for documenting the text mining requirements in detail?

– What are the long-term text mining goals?

Gender bias Critical Criteria:

Mix Gender bias quality and clarify ways to gain access to competitive Gender bias services.

– What is our formula for success in text mining ?

Copyright Directive Critical Criteria:

Examine Copyright Directive planning and innovate what needs to be done with Copyright Directive.

– Does text mining include applications and information with regulatory compliance significance (or other contractual conditions that must be formally complied with) in a new or unique manner for which no approved security requirements, templates or design models exist?

– What are our best practices for minimizing text mining project risk, while demonstrating incremental value and quick wins throughout the text mining project lifecycle?

National Centre for Text Mining Critical Criteria:

Confer re National Centre for Text Mining planning and probe National Centre for Text Mining strategic alliances.

– What tools do you use once you have decided on a text mining strategy and more importantly how do you choose?

Full text search Critical Criteria:

Analyze Full text search goals and change contexts.

– Why should we adopt a text mining framework?

– How do we go about Securing text mining?

Named entity recognition Critical Criteria:

Unify Named entity recognition quality and ask questions.

– Does text mining appropriately measure and monitor risk?

European Commission Critical Criteria:

Think about European Commission projects and secure European Commission creativity.

Fair use Critical Criteria:

Devise Fair use tasks and look in other fields.

Machine learning Critical Criteria:

Graph Machine learning tasks and probe Machine learning strategic alliances.

– What are the long-term implications of other disruptive technologies (e.g., machine learning, robotics, data analytics) converging with blockchain development?

– What knowledge, skills and characteristics mark a good text mining project manager?

– What new services of functionality will be implemented next with text mining ?

Structured data Critical Criteria:

Learn from Structured data governance and inform on and uncover unspoken needs and breakthrough Structured data results.

– 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 likely is the current text mining plan to come in on schedule or on budget?

– Should you use a hierarchy or would a more structured database-model work best?

Plain text Critical Criteria:

Administer Plain text engagements and assess and formulate effective operational and Plain text strategies.

– How do we Improve text mining service perception, and satisfaction?

Internet news Critical Criteria:

Conceptualize Internet news management and change contexts.

– Who is the main stakeholder, with ultimate responsibility for driving text mining forward?

Commercial software Critical Criteria:

Closely inspect Commercial software tasks and optimize Commercial software leadership as a key to advancement.

– What may be the consequences for the performance of an organization if all stakeholders are not consulted regarding text mining?

– Does text mining systematically track and analyze outcomes for accountability and quality improvement?

Text Analysis Portal for Research Critical Criteria:

Consider Text Analysis Portal for Research failures and adjust implementation of Text Analysis Portal for Research.

– Do those selected for the text mining team have a good general understanding of what text mining is all about?

– Is text mining dependent on the successful delivery of a current project?

Lexical analysis Critical Criteria:

Conceptualize Lexical analysis goals and adopt an insight outlook.

– 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?

Biomedical text mining Critical Criteria:

Merge Biomedical text mining quality and find the ideas you already have.

Social media Critical Criteria:

Examine Social media engagements and report on setting up Social media without losing ground.

– In the past year, have companies generally improved or worsened in terms of how quickly you feel they respond to you over social media channels surrounding a general inquiry or complaint?

– When you use social media to complain about a Customer Service issue, how often do you feel you get an answer or your complaint is resolved by the company?

– At what point will vulnerability assessments be performed once text mining is put into production (e.g., ongoing Risk Management after implementation)?

– Are business intelligence solutions starting to include social media data and analytics features?

– What methodology do you use for measuring the success of your social media programs for clients?

– What is our approach to Risk Management in the specific area of social media?

– What is the best way to integrate social media into existing CRM strategies?

– How have you defined R.O.I. from a social media perspective in the past?

– How important is real time for providing social media Customer Service?

– Do you have any proprietary tools or products related to social media?

– What social media dashboards are available and how do they compare?

– Do you offer social media training services for clients?

– How does social media redefine business intelligence?

– How is social media changing category management?

Copyright law of Japan Critical Criteria:

Scan Copyright law of Japan results and drive action.

– Think about the kind of project structure that would be appropriate for your text mining project. should it be formal and complex, or can it be less formal and relatively simple?


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 |

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.

External links:

To address the criteria in this checklist, these selected resources are provided for sources of further research and information:

text mining External links:

Text Mining / Text Analytics Specialist – bigtapp

Text Mining – AbeBooks

Text Mining – FREE download Text Mining

Database Directive External links:

European Union Database Directive –

text mining External links:

Text Mining / Text Analytics Specialist – bigtapp

Text Mining – AbeBooks

[PDF]Text Mining – UP – TextMining.pdf

Corpus manager External links:

Virtual Corpus Manager – Archive of Department of …

Corpus manager – manager&item_type=topic

Psychological profiling External links:

Psychological Profiling Flashcards | Quizlet

Psychological Profiling – VisualDNA

Pedophilia and Psychological Profiling

Customer attrition External links:

Listening to Feedback Is How You Fight Customer Attrition

Research Council External links:

Family Research Council – SourceWatch

GMRC Gas Machinery Conference – Gas Machinery Research Council

Pension Research Council

Semantic web External links:

Semantic Web Working Group SPARQL endpoint

Semantic Web Company Home – Semantic Web Company

Tribune Company External links:

Tribune Company – The New York Times


Concept mining External links:

Concept mining –

Concept Mining using Conceptual Ontological Graph …

Ronen Feldman External links:

Ronen Feldman’s Phone Number, Email, Address – Spokeo

Ronen Feldman | Amenity Analytics |

Author Page for Ronen Feldman :: SSRN

Social sciences External links:

Office of Behavioral and Social Sciences Research – Home

Frontpage – Social Sciences

College of Humanities and Social Sciences

Document processing External links:

Document Processing Technology | Meet the People | Parascript

Document Processing Specialist Jobs, Employment |

Document Outsourcing | Document Processing | Novitex

Information extraction External links:

[PDF]Title: Information Extraction from Muon …

Information extraction
http://Information extraction (IE) is the task of automatically extracting structured information from unstructured and/or semi-structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language processing (NLP).

Information extraction — NYU Scholars

Competitive Intelligence External links:

Proactive Worldwide – Competitive Intelligence … – Competitive Intelligence Software

Competitive Intelligence and Knowledge Management …

Open source External links:

Open source
http://In production and development, open source as a development model promotes a universal access via a free license to a product’s design or blueprint, and universal redistribution of that design or blueprint, including subsequent improvements to it by anyone. Before the phrase open source became widely adopted, developers and producers used a variety of other terms. Open source gained hold with the rise of the Internet, and the attendant need for massive retooling of the computing source code. Opening the source code enabled a self-enhancing diversity of production models, communication paths, and interactive communities. The open-source software movement arose to clarify the environment that the new copyright, licensing, domain, and consumer issues created. Generally, open source refers to a computer program in which the source code is available to the general public for use and/or modification from its original design. Open-source code is typically a collaborative effort where programmers improve upon the source code and share the changes within the community so that other members can help improve it further.

iSpy: Open Source Camera Security Software

Bitcoin – Open source P2P money

Text categorization External links:

Text categorization – Scholarpedia

Text categorization – Scholarpedia

What is Text Categorization | IGI Global

Part of speech tagging External links:

Part of speech tagging of Levantine [eScholarship]

National Diet Library External links:

Opening Hours & Library Holidays|National Diet Library

National Diet Library | library, Tokyo, Japan |

National Diet Library law. (Book, 1961) []

Predictive analytics External links:

Customer Analytics & Predictive Analytics Tools for Business

Inventory Optimization for Retail | Predictive Analytics

Stategic Location Management & Predictive Analytics | …

Content analysis External links:

[PDF]Three Approaches to Qualitative Content Analysis – …

Content Analysis – SEO Review Tools

Content analysis: Introduction – UC Davis, Psychology

Ad serving External links:

NUI Media – Ad Serving | Digital Media | Development

AdGlare | AdServer Platform & Ad Serving Software

We do ad serving software right | OrbitSoft

Sequential pattern mining External links:

[PDF]Sequential Pattern Mining – Home | College of Computing

Record linkage External links:

Record linkage – WIREs Computational Statistics › … › Vol 2 Issue 5 (September/October 2010)

“Record Linkage” by Stasha Ann Bown Larsen

CiteSeerX — Record Linkage: Current Practice and …

Google Book Search Settlement Agreement External links:

Google Book Search Settlement Agreement – …

Google Book Search Settlement Agreement – Revolvy Book Search Settlement Agreement

Pattern recognition External links:

Pattern recognition (Computer file, 2006) []

Pattern Recognition – Official Site

Pattern Recognition — Alexander Whitley

Exploratory data analysis External links:

1. Exploratory Data Analysis

Exploratory Data Analysis With R – Online Course | Udacity–ud651

Exploratory Data Analysis with R | Pluralsight

Spam filter External links:

The Best Spam Filters | Top Ten Reviews

Visionary Communications – Spam Filter Login

Configure your spam filter policies: Exchange Online Help

Name resolution External links:

Microsoft TCP/IP Host Name Resolution Order

NetBIOS Name Resolution –

[PDF]Enterprise Name Resolution Procedure –

Information Awareness Office External links:

Information Awareness Office –

Information Awareness Office – SourceWatch

Business intelligence External links:

Oracle Business Intelligence

Mortgage Business Intelligence Software :: Motivity Solutions

List of Business Intelligence Skills – The Balance

Sentiment Analysis External links:

YUKKA Lab – Sentiment Analysis

Business rule External links:

Business Rules vs. Business Requirements …

[PDF]Business Rule Number – Internal Revenue Service

Gender bias External links:

Gender bias legal definition of gender bias – Legal Dictionary

Title IX and Gender Bias in Language – CourseBB

Free gender bias Essays and Papers – 123HelpMe

Copyright Directive External links:

[PDF]Implementing the EU Copyright Directive

National Centre for Text Mining External links:

John McNaught | National Centre for Text Mining | …

National Centre for Text Mining – Revolvy Centre for Text Mining

CiteSeerX — National Centre for Text Mining

Full text search External links:

FDIC: Full Text Search

Named entity recognition External links:

NAMED ENTITY RECOGNITION – Microsoft Corporation

Create an OpenNLP model for Named Entity Recognition …


European Commission External links:

European commission | World | The Guardian

Fair use External links:

What is fair use? – Definition from

About the Fair Use Index | U.S. Copyright Office

Cases Archive – Stanford Copyright and Fair Use Center

Machine learning External links:

Microsoft Azure Machine Learning Studio

Structured data External links:

Introduction to Structured Data | Search | Google Developers

CLnet Solution Sdn Bhd | Structured Data Cabling Malaysia | What Is Structured Data?

Plain text External links:

Mobility Disabilities | Plain Text | Walt Disney World Resort

Plain Text

Communications / 2017- 2018 Plain Text Calendar

Internet news External links:

Sci Burg – The Latest Internet News World | Sci Burg – Home | Latest Internet News World

Vin Zite – Latest Internet News World

Commercial software External links:

ISTE | Commercial Software Programs Approved for …

What is commercial software –

E-file approved commercial software providers for …

Text Analysis Portal for Research External links:

TAPoR: Text Analysis Portal for Research | arts … – TAPoR – Text Analysis Portal for Research TAPoR – Text Analysis Portal for Research

Lexical analysis External links:

Lexical Analysis With Flex, for Flex 2.6.3: Rules Section

[PDF]Lexical Analysis: DFA Minimization & Wrap Up

Biomedical text mining External links:

Biomedical Text Mining Applied To Document Retrieval …

Social media External links:

A Unified Social Media Management Platform – Statusbrew

SOCi Social Media Marketing & Management Platform

Copyright law of Japan External links:

Copyright Law of Japan | e-Asia

Leave a Reply

Your email address will not be published. Required fields are marked *