215 Text Analytics Success Criteria

What is involved in Text Analytics

Find out what the related areas are that Text Analytics 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 Analytics thinking-frame.

How far is your company on its Text Analytics journey?

Take this short survey to gauge your organization’s progress toward Text Analytics 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 Analytics related domains to cover and 215 essential critical questions to check off in that domain.

The following domains are covered:

Text Analytics, Lexical analysis, Predictive analytics, Information retrieval, Google Book Search Settlement Agreement, Plain text, Text clustering, Ad serving, Gender bias, Predictive classification, Web mining, Full text search, Information visualization, Semantic web, Spam filter, Customer relationship management, Text corpus, Concept mining, Content analysis, Text Analytics, Research Council, Record linkage, Business intelligence, Ronen Feldman, Copyright Directive, Limitations and exceptions to copyright, Social sciences, Information Awareness Office, Sequential pattern mining, Exploratory data analysis, Pattern recognition, Copyright law of Japan, Psychological profiling, Text mining, Document processing, National Diet Library, Information extraction, Big data, European Commission, Market sentiment, Text categorization, Part of speech tagging, Name resolution, Biomedical text mining, Named entity recognition, Data mining, Social media, PubMed Central, Hargreaves review, Competitive Intelligence, Fair use, Intelligence analyst, Business rule, Text Analysis Portal for Research, News analytics, Noun phrase, Tribune Company:

Text Analytics Critical Criteria:

Discourse Text Analytics outcomes and find answers.

– What other organizational variables, such as reward systems or communication systems, affect the performance of this Text Analytics process?

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

– How does the organization define, manage, and improve its Text Analytics processes?

– Have text analytics mechanisms like entity extraction been considered?

Lexical analysis Critical Criteria:

Talk about Lexical analysis visions and adjust implementation of Lexical analysis.

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

– Do we have past Text Analytics Successes?

Predictive analytics Critical Criteria:

Communicate about Predictive analytics outcomes and arbitrate Predictive analytics techniques that enhance teamwork and productivity.

– In the case of a Text Analytics project, the criteria for the audit derive from implementation objectives. an audit of a Text Analytics project involves assessing whether the recommendations outlined for implementation have been met. in other words, can we track that any Text Analytics project is implemented as planned, and is it working?

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

– How will we insure seamless interoperability of Text Analytics moving forward?

– Do we all define Text Analytics in the same way?

Information retrieval Critical Criteria:

See the value of Information retrieval goals and explore and align the progress in Information retrieval.

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

– Is there a Text Analytics Communication plan covering who needs to get what information when?

– What tools and technologies are needed for a custom Text Analytics project?

Google Book Search Settlement Agreement Critical Criteria:

Facilitate Google Book Search Settlement Agreement issues and correct better engagement with Google Book Search Settlement Agreement results.

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

– What are your most important goals for the strategic Text Analytics objectives?

– What are the record-keeping requirements of Text Analytics activities?

Plain text Critical Criteria:

Accommodate Plain text management and improve Plain text service perception.

– What potential environmental factors impact the Text Analytics effort?

– Does our organization need more Text Analytics education?

– How do we keep improving Text Analytics?

Text clustering Critical Criteria:

Reconstruct Text clustering adoptions and attract Text clustering skills.

– What role does communication play in the success or failure of a Text Analytics project?

– What are the barriers to increased Text Analytics production?

– Who sets the Text Analytics standards?

Ad serving Critical Criteria:

Weigh in on Ad serving tactics and innovate what needs to be done with Ad serving.

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

– How do we go about Securing Text Analytics?

Gender bias Critical Criteria:

Powwow over Gender bias tactics and customize techniques for implementing Gender bias controls.

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

– How do we go about Comparing Text Analytics approaches/solutions?

Predictive classification Critical Criteria:

Boost Predictive classification goals and look for lots of ideas.

– Who is the main stakeholder, with ultimate responsibility for driving Text Analytics forward?

– What are the Key enablers to make this Text Analytics move?

Web mining Critical Criteria:

Have a round table over Web mining tactics and describe which business rules are needed as Web mining interface.

– Which customers cant participate in our Text Analytics domain because they lack skills, wealth, or convenient access to existing solutions?

– Are there any disadvantages to implementing Text Analytics? There might be some that are less obvious?

– Do the Text Analytics decisions we make today help people and the planet tomorrow?

Full text search Critical Criteria:

Chat re Full text search quality and intervene in Full text search processes and leadership.

– Who are the people involved in developing and implementing Text Analytics?

– How can you measure Text Analytics in a systematic way?

Information visualization Critical Criteria:

Add value to Information visualization engagements and cater for concise Information visualization education.

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

– How do senior leaders actions reflect a commitment to the organizations Text Analytics values?

– How is the value delivered by Text Analytics being measured?

Semantic web Critical Criteria:

Have a round table over Semantic web failures and check on ways to get started with Semantic web.

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

Spam filter Critical Criteria:

Paraphrase Spam filter quality and point out improvements in Spam filter.

– Is Text Analytics dependent on the successful delivery of a current project?

– Is Supporting Text Analytics documentation required?

Customer relationship management Critical Criteria:

Distinguish Customer relationship management leadership and interpret which customers can’t participate in Customer relationship management because they lack skills.

– Has your organization ever had to invoke its disaster recovery plan which included the CRM solution and if so was the recovery time objective met and how long did it take to return to your primary solution?

– Support – how can we drive support for using the escalation processes for service, support and billing issues?

– In the case of system downtime that exceeds an agreed-upon SLA, what remedies do you provide?

– How do you enhance existing cache management techniques for context-dependent data?

– Have you integrated your call center telephony to your crm application?

– Does the user have permission to synchronize to the offline data store?

– Job Satisfaction and Job performance: Is the relationship spurious?

– How many current users will maintain and access the CRM program?

– Can you identify your customers when they visit your website?

– How is a typical client engagement with your firm structured?

– What were the factors that caused CRM to appear when it did?

– What are the necessary steps to evaluate a CRM solution?

– What is the Impact of Social CRM on Customer Support?

– Does Customer Knowledge Affect How Loyalty Is Formed?

– What is the products current release level/version?

– What system will the data come from?

– What happens to reports?

– Why Multi-Channel CRM?

– What is on-demand CRM?

– Why Web-based CRM?

Text corpus Critical Criteria:

Sort Text corpus strategies and create Text corpus explanations for all managers.

– How will you know that the Text Analytics project has been successful?

Concept mining Critical Criteria:

Check Concept mining issues and secure Concept mining creativity.

– Is a Text Analytics Team Work effort in place?

– What is our Text Analytics Strategy?

– How to Secure Text Analytics?

Content analysis Critical Criteria:

Use past Content analysis visions and be persistent.

– Do we monitor the Text Analytics decisions made and fine tune them as they evolve?

– Which individuals, teams or departments will be involved in Text Analytics?

Text Analytics Critical Criteria:

Consolidate Text Analytics failures and overcome Text Analytics skills and management ineffectiveness.

– What are your current levels and trends in key measures or indicators of Text Analytics 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?

– Marketing budgets are tighter, consumers are more skeptical, and social media has changed forever the way we talk about Text Analytics. How do we gain traction?

– Does the Text Analytics task fit the clients priorities?

Research Council Critical Criteria:

Differentiate Research Council tasks and explain and analyze the challenges of Research Council.

– What management system can we use to leverage the Text Analytics experience, ideas, and concerns of the people closest to the work to be done?

– How can we improve Text Analytics?

Record linkage Critical Criteria:

Examine Record linkage engagements and tour deciding if Record linkage progress is made.

– What is our formula for success in Text Analytics ?

– What about Text Analytics Analysis of results?

– Why are Text Analytics skills important?

Business intelligence Critical Criteria:

Administer Business intelligence engagements and find out.

– What are the potential areas of conflict that can arise between organizations IT and marketing functions around the deployment and use of business intelligence and data analytics software services and what is the best way to resolve them?

– Does the software allow users to bring in data from outside the company on-the-flylike demographics and market research to augment corporate data?

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

– Does your bi software work well with both centralized and decentralized data architectures and vendors?

– 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 is the difference between Data analytics and Business Analytics If Any?

– Does your BI solution help you find the right views to examine your data?

– What should recruiters look for in a business intelligence professional?

– What tools are there for publishing sharing and visualizing data online?

– Number of data sources that can be simultaneously accessed?

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

– Are there any on demand analytics tools in the cloud?

– Is your software easy for IT to manage and upgrade?

– Do we offer a good introduction to data warehouse?

– Describe any training materials offered?

– Is your BI software easy to understand?

– Do you support video integration?

Ronen Feldman Critical Criteria:

Interpolate Ronen Feldman adoptions and use obstacles to break out of ruts.

– Have you identified your Text Analytics key performance indicators?

– Think of your Text Analytics project. what are the main functions?

Copyright Directive Critical Criteria:

Huddle over Copyright Directive management and find the ideas you already have.

– Record-keeping requirements flow from the records needed as inputs, outputs, controls and for transformation of a Text Analytics process. ask yourself: are the records needed as inputs to the Text Analytics process available?

– Are accountability and ownership for Text Analytics clearly defined?

– How much does Text Analytics help?

Limitations and exceptions to copyright Critical Criteria:

Adapt Limitations and exceptions to copyright decisions and explain and analyze the challenges of Limitations and exceptions to copyright.

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

– What are the disruptive Text Analytics technologies that enable our organization to radically change our business processes?

Social sciences Critical Criteria:

Have a session on Social sciences visions and devote time assessing Social sciences and its risk.

– How do we Improve Text Analytics service perception, and satisfaction?

– Have all basic functions of Text Analytics been defined?

Information Awareness Office Critical Criteria:

Test Information Awareness Office strategies and gather Information Awareness Office models .

– Is maximizing Text Analytics protection the same as minimizing Text Analytics loss?

– What are the usability implications of Text Analytics actions?

Sequential pattern mining Critical Criteria:

Familiarize yourself with Sequential pattern mining adoptions and catalog Sequential pattern mining activities.

– What are all of our Text Analytics domains and what do they do?

Exploratory data analysis Critical Criteria:

Read up on Exploratory data analysis visions and assess and formulate effective operational and Exploratory data analysis strategies.

– Is Text Analytics Required?

Pattern recognition Critical Criteria:

Sort Pattern recognition management and assess and formulate effective operational and Pattern recognition strategies.

Copyright law of Japan Critical Criteria:

Survey Copyright law of Japan planning and point out improvements in Copyright law of Japan.

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

– What vendors make products that address the Text Analytics needs?

Psychological profiling Critical Criteria:

Focus on Psychological profiling management and intervene in Psychological profiling processes and leadership.

– Why is it important to have senior management support for a Text Analytics project?

Text mining Critical Criteria:

Co-operate on Text mining results and create Text mining explanations for all managers.

– Are assumptions made in Text Analytics stated explicitly?

– Who will provide the final approval of Text Analytics deliverables?

Document processing Critical Criteria:

Revitalize Document processing risks and point out Document processing tensions in leadership.

– Can we do Text Analytics without complex (expensive) analysis?

National Diet Library Critical Criteria:

Distinguish National Diet Library management and revise understanding of National Diet Library architectures.

– what is the best design framework for Text Analytics organization now that, in a post industrial-age if the top-down, command and control model is no longer relevant?

– Do several people in different organizational units assist with the Text Analytics process?

– How do we maintain Text Analyticss Integrity?

Information extraction Critical Criteria:

Merge Information extraction strategies and probe Information extraction strategic alliances.

– Think about the people you identified for your Text Analytics 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?

– What are our Text Analytics Processes?

Big data Critical Criteria:

Wrangle Big data tasks and oversee implementation of Big data.

– 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 are the particular research needs of your organization on big data analytics that you find essential to adequately handle your data assets?

– Does big data threaten the traditional data warehouse business intelligence model stack?

– Do we understand public perception of transportation service delivery at any given time?

– Wheres the evidence that using big data intelligently will improve business performance?

– From what sources does your organization collect, or expects to collect, data?

– Are there any best practices or standards for the use of Big Data solutions?

– Which other Oracle Business Intelligence products are used in your solution?

– What new Security and Privacy challenge arise from new Big Data solutions?

– What are the new applications that are enabled by Big Data solutions?

– Is data-driven decision-making part of the organizations culture?

– How fast can we determine changes in the incoming data?

– Isnt big data just another way of saying analytics?

– Which Oracle applications are used in your project?

– Is our data collection and acquisition optimized?

– What if the data cannot fit on your computer?

– What is the cost of partitioning/balancing?

– How do we measure value of an analytic?

– How to deal with too much data?

European Commission Critical Criteria:

Accelerate European Commission adoptions and catalog what business benefits will European Commission goals deliver if achieved.

Market sentiment Critical Criteria:

Adapt Market sentiment leadership and find answers.

– Is the scope of Text Analytics defined?

Text categorization Critical Criteria:

Recall Text categorization projects and find the ideas you already have.

– Meeting the challenge: are missed Text Analytics opportunities costing us money?

– What will drive Text Analytics change?

Part of speech tagging Critical Criteria:

See the value of Part of speech tagging tasks and explore and align the progress in Part of speech tagging.

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

– To what extent does management recognize Text Analytics as a tool to increase the results?

– Are there Text Analytics problems defined?

Name resolution Critical Criteria:

Focus on Name resolution tactics and mentor Name resolution customer orientation.

– Among the Text Analytics product and service cost to be estimated, which is considered hardest to estimate?

– Will new equipment/products be required to facilitate Text Analytics delivery for example is new software needed?

Biomedical text mining Critical Criteria:

Incorporate Biomedical text mining goals and define what do we need to start doing with Biomedical text mining.

– Can Management personnel recognize the monetary benefit of Text Analytics?

Named entity recognition Critical Criteria:

Boost Named entity recognition outcomes and adjust implementation of Named entity recognition.

– How do we know that any Text Analytics analysis is complete and comprehensive?

Data mining Critical Criteria:

Investigate Data mining outcomes and define Data mining competency-based leadership.

– 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?

– When a Text Analytics manager recognizes a problem, what options are available?

– What programs do we have to teach data mining?

Social media Critical Criteria:

Deduce Social media issues and reduce Social media costs.

– 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?

– Think about the functions involved in your Text Analytics project. what processes flow from these functions?

– 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?

– Which of the following are reasons you use social media when it comes to Customer Service?

– How would our PR, marketing, and social media change if we did not use outside agencies?

– 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?

– Do you offer social media training services for clients?

– How does social media redefine business intelligence?

– How is social media changing category management?

– Is social media a better investment than SEO?

– How to deal with Text Analytics Changes?

PubMed Central Critical Criteria:

Detail PubMed Central decisions and look in other fields.

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

– What are internal and external Text Analytics relations?

Hargreaves review Critical Criteria:

Systematize Hargreaves review results and mentor Hargreaves review customer orientation.

– What is the purpose of Text Analytics in relation to the mission?

– What are the Essentials of Internal Text Analytics Management?

Competitive Intelligence Critical Criteria:

Exchange ideas about Competitive Intelligence management and gather practices for scaling Competitive Intelligence.

– What knowledge, skills and characteristics mark a good Text Analytics project manager?

– Have the types of risks that may impact Text Analytics been identified and analyzed?

Fair use Critical Criteria:

Collaborate on Fair use governance and tour deciding if Fair use progress is made.

– What threat is Text Analytics addressing?

Intelligence analyst Critical Criteria:

Merge Intelligence analyst decisions and create a map for yourself.

– What are the key skills a Business Intelligence Analyst should have?

Business rule Critical Criteria:

Exchange ideas about Business rule leadership and perfect Business rule conflict management.

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

Text Analysis Portal for Research Critical Criteria:

Grasp Text Analysis Portal for Research planning and inform on and uncover unspoken needs and breakthrough Text Analysis Portal for Research results.

– Does Text Analytics 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?

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

– What are our needs in relation to Text Analytics skills, labor, equipment, and markets?

News analytics Critical Criteria:

Reorganize News analytics planning and diversify by understanding risks and leveraging News analytics.

– How do we Lead with Text Analytics in Mind?

Noun phrase Critical Criteria:

Be clear about Noun phrase adoptions and develop and take control of the Noun phrase initiative.

– How do we measure improved Text Analytics service perception, and satisfaction?

– How do we Identify specific Text Analytics investment and emerging trends?

– Are there recognized Text Analytics problems?

Tribune Company Critical Criteria:

Substantiate Tribune Company planning and question.

– Think about the kind of project structure that would be appropriate for your Text Analytics 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 Analytics 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.

External links:

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

Text Analytics External links:

[PDF]Syllabus Course Title: Text Analytics – Regis University

How to Use Text Analytics in Business – Data Informed

Text analytics software| NICE LTD | NICE

Lexical analysis External links:

Lexical analysis – How is Lexical analysis abbreviated?

c – Question on lexical analysis – Stack Overflow

Compiler 1 lexical analysis – YouTube

Predictive analytics External links:

Predictive Analytics for Healthcare | Forecast Health

Predictive Analytics Software, Social Listening | NewBrand

Predictive Analytics Workers Compensation

Information retrieval External links:

Introduction to Information Retrieval

PPIRS – Past Performance Information Retrieval System

[PDF]Introduction to Information Retrieval

Google Book Search Settlement Agreement External links:

Google Book Search Settlement Agreement – …

Plain text External links:

How to view all e-mail messages in plain text format

How to Use TextEdit Plain Text Mode by Default in Mac OS X

Mobility Disabilities | Plain Text | Walt Disney World Resort

Text clustering External links:

Text Clustering Case Study – Scribd

Ad serving External links:

Powerful Ad Serving Simplified – AdButler

What’s New in Ad Serving Technology | Sovrn

How Does Ad Serving Work? – Ad Ops Insider

Gender bias External links:

Free gender bias Essays and Papers – 123HelpMe

Title IX and Gender Bias in Language – CourseBB

What is Gender Bias? (with pictures) – wiseGEEK

Predictive classification External links:


Web mining External links:

Minero – Monero Web Mining


CSE 258 – Recommender Sys&Web Mining – LE [A00] – …

Full text search External links:

FDIC: Full Text Search

Does Oracle support full text search? – Stack Overflow

Full Text Search of PDF using Adobe Acrobat

Information visualization External links:

Information Visualization: What is Information Visualization?

Information visualization (Book, 2001) [WorldCat.org]

Information visualization (Book, 2017) [WorldCat.org]

Semantic web External links:

Content Writing in the Semantic Web | Udemy

Semantic Web Company Home – Semantic Web Company

Semantic Web Flashcards | Quizlet

Spam filter External links:

Visionary Communications – Spam Filter Login

The Best Spam Filters | Top Ten Reviews

BestWeb Spam Filter: Welcome

Customer relationship management External links:

Oracle – Siebel Customer Relationship Management

1workforce – Customer Relationship Management …

Salesnet CRM Solutions | Customer Relationship Management

Text corpus External links:

The Electronic Text Corpus of Sumerian Royal Inscriptions

Full-Text Corpus | Nickels and Dimes

Concept mining External links:

Concept Mining Inc in Princeton, WV with Reviews – YP.com

[PDF]Streaming Hierarchical Clustering for Concept Mining

Concept Mining using Conceptual Ontological Graph …

Content analysis External links:

Content analysis: Introduction – UC Davis, Psychology

[PDF]Three Approaches to Qualitative Content Analysis – …

Vision API – Image Content Analysis | Google Cloud Platform

Text Analytics External links:

How to Use Text Analytics in Business – Data Informed

Text Mining / Text Analytics Specialist – bigtapp

[PDF]Syllabus Course Title: Text Analytics – Regis University

Research Council External links:

Family Research Council Corporate Portal

Family Research Council – Home | Facebook

Homosexuality – Family Research Council

Record linkage External links:

“Record Linkage” by Stasha Ann Bown Larsen

Record linkage (eBook, 1946) [WorldCat.org]

Business intelligence External links:

List of Business Intelligence Skills – The Balance

GENCO Business Intelligence Gateway

Ronen Feldman External links:

Prof. Ronen Feldman – huji.ac.il

Ronen Feldman – Google Scholar Citations

Ronen Feldman | Facebook

Copyright Directive External links:

[PDF]Implementing the EU Copyright Directive

Copyright Directive – WOW.com

Social sciences External links:

University of Maryland College of Behavioral and Social Sciences …

School of Social Sciences

College of Humanities and Social Sciences

Information Awareness Office External links:

information awareness office – projectcensored.org

Information Awareness Office – SourceWatch

Information Awareness Office (IAO): How’s This for …

Sequential pattern mining External links:

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

[PDF]Sequential PAttern Mining using A Bitmap …

Exploratory data analysis External links:

What Is Exploratory Data Analysis? – DZone Big Data

[PDF]Principles and Procedures of Exploratory Data Analysis

1. Exploratory Data Analysis

Pattern recognition External links:

Chart Pattern Recognition – Identifying The Flag Pattern

Pattern recognition – Encyclopedia of Mathematics

Pattern recognition (Computer file, 2006) [WorldCat.org]

Copyright law of Japan External links:

“Copyright law of Japan” on Revolvy.com
topics.revolvy.com/topic/Copyright law of Japan


Copyright Law of Japan | e-Asia

Psychological profiling External links:

Psychological Profiling Flashcards | Quizlet

Pedophilia and Psychological Profiling

Text mining External links:

Text Mining Specialist Jobs, Employment | Indeed.com

Text Mining / Text Analytics Specialist – bigtapp

Text Mining in Excel | Count Words in Spreadsheets

Document processing External links:

LINGO – Web Based EDI Document Processing


BPOs: Future of Document Processing is Automation | …

National Diet Library External links:

Opening Hours & Library Holidays|National Diet Library

Online Gallery | National Diet Library

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

Information extraction External links:

[PDF]Information Extraction – Brigham Young University

CiteSeerX — Information Extraction

Information Extraction
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).

Big data External links:

Swiftly – Leverage big data to move your city

ZestFinance.com: Machine Learning & Big Data …

Big Data Partnership – Official Site

European Commission External links:

RoHS 2 – Electronics waste – Environment – European Commission

European Commission : CORDIS : Home

Nivolumab Approved for Bladder Cancer by European Commission

Market sentiment External links:

Stock Market Sentiment Indicators – sentimenTrader

WhisperNumber.com / Market Sentiment LLC

Delta Tactical Market Sentiment – Barron’s

Text categorization External links:

What is Text Categorization | IGI Global

Text categorization – Scholarpedia

Text categorization – Scholarpedia

Part of speech tagging External links:


Name resolution External links:

The Cable Guy – The Name Resolution Policy Table

Microsoft TCP/IP Host Name Resolution Order

Biomedical text mining External links:

Biomedical Text Mining and Its Applications – PLOS

What is Biomedical text mining? – Quora

SparkText: Biomedical Text Mining on Big Data Framework.

Named entity recognition External links:


Tagging, Chunking & Named Entity Recognition with NLTK

NAMED ENTITY RECOGNITION – Microsoft Corporation

Data mining External links:

Data mining techniques (Book, 2002) [WorldCat.org]

Title Data Mining Jobs, Employment | Indeed.com

UT Data Mining

Social media External links:

A Unified Social Media Management Platform – Statusbrew

Social Media Engagement App | Post Planner

Tools to Boost Your Social Media Productivity

PubMed Central External links:

PubMed Central | NIH Library

PubMed Central | Rutgers University Libraries

MEDLINE, PubMed, and PMC (PubMed Central): How are …

Hargreaves review External links:

Rowan Misty Pattern Book by Kim Hargreaves Review – …

Competitive Intelligence External links:

Follow.net – Competitive Intelligence Software

Proactive Worldwide – Competitive Intelligence …

Fair use External links:

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

Stanford Copyright and Fair Use Center

Intelligence analyst External links:

Intelligence Analyst Jobs | Indeed.com

What does an Intelligence Analyst do? – Sokanu

Intelligence Analyst Jobs in Washington, D.C. – ClearanceJobs

Business rule External links:

Business Rules vs. Business Requirements …

Text Analysis Portal for Research External links:

tapor.ca – TAPoR – Text Analysis Portal for Research

tapor.ca : TAPoR – Text Analysis Portal for Research

tapor.ca – TAPoR – Text Analysis Portal for Research

News analytics External links:

Yakshof – Big Data News Analytics

Noun phrase External links:

Grammar Bytes! :: The Noun Phrase

The noun phrase | TeachingEnglish | British Council | BBC

The noun phrase (Book, 2002) [WorldCat.org]