198 Extremely Powerful Text Analytics Questions You Do Not Know

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 198 essential critical questions to check off in that domain.

The following domains are covered:

Text Analytics, National Security, Market sentiment, Fair use, Record linkage, Copyright Directive, PubMed Central, Commercial software, Document processing, Scientific discovery, UC Berkeley School of Information, Information extraction, Structured data, Text Analytics, National Institutes of Health, Google Book Search Settlement Agreement, Social media, Text Analysis Portal for Research, Information visualization, Text mining, Data mining, Named entity recognition, Predictive classification, Ad serving, Big data, Pattern recognition, Text categorization, Semantic web, Sentiment Analysis, Customer attrition, Intelligence analyst, Hargreaves review, Spam filter, Corpus manager, Security appliance, European Commission, Customer relationship management, Internet news, Document summarization, Information retrieval, Competitive Intelligence, Research Council, Psychological profiling, Part of speech tagging, Limitations and exceptions to copyright, Gender bias, Predictive analytics, National Diet Library, News analytics, Plain text, Document Type Definition, Business intelligence, Concept mining, Copyright law of Japan:

Text Analytics Critical Criteria:

Examine Text Analytics adoptions and transcribe Text Analytics as tomorrows backbone for success.

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

– Have text analytics mechanisms like entity extraction been considered?

– How will you measure your Text Analytics effectiveness?

– Are there Text Analytics problems defined?

National Security Critical Criteria:

Check National Security decisions and revise understanding of National Security architectures.

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

– What are the top 3 things at the forefront of our Text Analytics agendas for the next 3 years?

– Does the Text Analytics task fit the clients priorities?

Market sentiment Critical Criteria:

Check Market sentiment risks and question.

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

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

– How do we maintain Text Analyticss Integrity?

Fair use Critical Criteria:

Judge Fair use projects and drive action.

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

– Is Text Analytics Realistic, or are you setting yourself up for failure?

– Are assumptions made in Text Analytics stated explicitly?

Record linkage Critical Criteria:

Chart Record linkage risks and explore and align the progress in Record linkage.

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

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

Copyright Directive Critical Criteria:

Think carefully about Copyright Directive leadership and track iterative Copyright Directive results.

– 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?

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

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

PubMed Central Critical Criteria:

Systematize PubMed Central goals and give examples utilizing a core of simple PubMed Central skills.

– 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?

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

– What is our formula for success in Text Analytics ?

Commercial software Critical Criteria:

Prioritize Commercial software issues and observe effective Commercial software.

– Do we aggressively reward and promote the people who have the biggest impact on creating excellent Text Analytics services/products?

– What are the Essentials of Internal Text Analytics Management?

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

Document processing Critical Criteria:

Disseminate Document processing results and develop and take control of the Document processing initiative.

– Will Text Analytics deliverables need to be tested and, if so, by whom?

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

– Which Text Analytics goals are the most important?

Scientific discovery Critical Criteria:

Huddle over Scientific discovery leadership and raise human resource and employment practices for Scientific discovery.

– 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?

– Do we have past Text Analytics Successes?

UC Berkeley School of Information Critical Criteria:

Communicate about UC Berkeley School of Information adoptions and plan concise UC Berkeley School of Information education.

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

– Have you identified your Text Analytics key performance indicators?

Information extraction Critical Criteria:

Disseminate Information extraction quality and customize techniques for implementing Information extraction controls.

– What are our Text Analytics Processes?

Structured data Critical Criteria:

Collaborate on Structured data decisions and proactively manage Structured data risks.

– 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)?

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

– What are the short and long-term Text Analytics goals?

– How do we go about Securing Text Analytics?

– How do we Lead with Text Analytics in Mind?

Text Analytics Critical Criteria:

Interpolate Text Analytics failures and do something to it.

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

– Is the scope of Text Analytics defined?

National Institutes of Health Critical Criteria:

Contribute to National Institutes of Health failures and find out.

– How do your measurements capture actionable Text Analytics information for use in exceeding your customers expectations and securing your customers engagement?

– How can the value of Text Analytics be defined?

Google Book Search Settlement Agreement Critical Criteria:

Co-operate on Google Book Search Settlement Agreement goals and simulate teachings and consultations on quality process improvement of Google Book Search Settlement Agreement.

– 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?

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

Social media Critical Criteria:

Consult on Social media results and innovate what needs to be done with Social media.

– 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?

– 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?

– What are the best practices for Risk Management in Social Media?

– Do you offer social media training services for clients?

Text Analysis Portal for Research Critical Criteria:

Focus on Text Analysis Portal for Research visions and inform on and uncover unspoken needs and breakthrough Text Analysis Portal for Research results.

– How do we manage Text Analytics Knowledge Management (KM)?

– Does Text Analytics appropriately measure and monitor risk?

Information visualization Critical Criteria:

Explore Information visualization goals and summarize a clear Information visualization focus.

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

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

Text mining Critical Criteria:

Understand Text mining goals and define what our big hairy audacious Text mining goal is.

– 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?

– How can we incorporate support to ensure safe and effective use of Text Analytics into the services that we provide?

Data mining Critical Criteria:

Boost Data mining adoptions and raise human resource and employment practices for Data mining.

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

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

– 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?

– What programs do we have to teach data mining?

Named entity recognition Critical Criteria:

Wrangle Named entity recognition visions and oversee Named entity recognition requirements.

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

Predictive classification Critical Criteria:

Scrutinze Predictive classification tasks and forecast involvement of future Predictive classification projects in development.

– How likely is the current Text Analytics plan to come in on schedule or on budget?

– How would one define Text Analytics leadership?

– Why are Text Analytics skills important?

Ad serving Critical Criteria:

Facilitate Ad serving quality and look in other fields.

– What is the total cost related to deploying Text Analytics, including any consulting or professional services?

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

Big data Critical Criteria:

Rank Big data issues and find out.

– 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?

– How we make effective use of the flood of data that will be produced will be a real big data challenge: should we keep it all or could we throw some away?

– Have we let algorithms and large centralized data centres not only control the remembering but also the meaning and interpretation of the data?

– Do we address the daunting challenge of Big Data: how to make an easy use of highly diverse data and provide knowledge?

– How should we organize to capture the benefit of Big Data and move swiftly to higher maturity stages?

– What type(s) of data does your organization find relevant but has not yet been able to exploit?

– Does the in situ hardware have the computational capacity to support such algorithms?

– 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?

– With more data to analyze, can Big Data improve decision-making?

– How to visualize non-numeric data, e.g. text, icons, or images?

– Are our business activities mainly conducted in one country?

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

– How do we measure the efficiency of these algorithms?

– How to model context in a computational environment?

– More efficient all-to-all operations (similarities)?

– What business challenges did you face?

– What are some impacts of Big Data?

– How robust are the results?

– How much data so far?

Pattern recognition Critical Criteria:

Pay attention to Pattern recognition visions and don’t overlook the obvious.

– Does Text Analytics analysis isolate the fundamental causes of problems?

Text categorization Critical Criteria:

Design Text categorization leadership and point out Text categorization tensions in leadership.

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

Semantic web Critical Criteria:

Analyze Semantic web tasks and visualize why should people listen to you regarding Semantic web.

– How can skill-level changes improve Text Analytics?

Sentiment Analysis Critical Criteria:

Experiment with Sentiment Analysis management and work towards be a leading Sentiment Analysis expert.

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

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

– What are current Text Analytics Paradigms?

Customer attrition Critical Criteria:

X-ray Customer attrition projects and differentiate in coordinating Customer attrition.

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

– What threat is Text Analytics addressing?

Intelligence analyst Critical Criteria:

Contribute to Intelligence analyst projects and inform on and uncover unspoken needs and breakthrough Intelligence analyst results.

– What is the difference between a data scientist and a business intelligence analyst?

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

– Is Text Analytics Required?

Hargreaves review Critical Criteria:

Check Hargreaves review leadership and tour deciding if Hargreaves review progress is made.

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

– How to deal with Text Analytics Changes?

Spam filter Critical Criteria:

Concentrate on Spam filter planning and visualize why should people listen to you regarding Spam filter.

Corpus manager Critical Criteria:

Start Corpus manager engagements and prioritize challenges of Corpus manager.

– What new services of functionality will be implemented next with Text Analytics ?

Security appliance Critical Criteria:

Mine Security appliance strategies and budget the knowledge transfer for any interested in Security appliance.

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

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

European Commission Critical Criteria:

Distinguish European Commission adoptions and innovate what needs to be done with European Commission.

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

– How much does Text Analytics help?

Customer relationship management Critical Criteria:

Guard Customer relationship management leadership and define Customer relationship management competency-based leadership.

– How many training hours are included within the standard support and maintenance agreement and how is that training delivered (e.g., at the vendors location, onsite at the customers location, via the web)?

– 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?

– Given that we simply do not have the resources to save all the data that comes into an organization, what shall be saved and what shall be lost?

– Do we understand our clients business drivers, financial metrics, buying process and decision criteria?

– Is the Customer Satisfaction Process something which you think can be automated via an IVR?

– Which Customers just take up resources and should be considered competitors?

– Do you follow-up with your customers after their order has been filled?

– What is the network quality, including speed and dropped packets?

– How often do you fully test your disaster recovery capabilities?

– Are the application host process shut-down options acceptable?

– How much data is the right amount of data to collect?

– How can mobile users access services transparently?

– What kinds of problems/issues do they encounter?

– How is Business Intelligence related to CRM?

– Is the offline database size acceptable?

– Are there multiple Outlook profiles?

– How much e-mail should be routed?

– What happens to workflows?

– Why Multi-Channel CRM?

Internet news Critical Criteria:

Meet over Internet news risks and look at it backwards.

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

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

Document summarization Critical Criteria:

Incorporate Document summarization results and handle a jump-start course to Document summarization.

Information retrieval Critical Criteria:

Closely inspect Information retrieval planning and perfect Information retrieval conflict management.

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

Competitive Intelligence Critical Criteria:

Exchange ideas about Competitive Intelligence engagements and pioneer acquisition of Competitive Intelligence systems.

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

– Have all basic functions of Text Analytics been defined?

Research Council Critical Criteria:

Consolidate Research Council quality and find out what it really means.

– 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?

– In what ways are Text Analytics vendors and us interacting to ensure safe and effective use?

Psychological profiling Critical Criteria:

Have a session on Psychological profiling risks and cater for concise Psychological profiling education.

Part of speech tagging Critical Criteria:

Categorize Part of speech tagging engagements and differentiate in coordinating Part of speech tagging.

– How do we make it meaningful in connecting Text Analytics with what users do day-to-day?

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

Limitations and exceptions to copyright Critical Criteria:

Think carefully about Limitations and exceptions to copyright tasks and plan concise Limitations and exceptions to copyright education.

– Does Text Analytics systematically track and analyze outcomes for accountability and quality improvement?

– Risk factors: what are the characteristics of Text Analytics that make it risky?

Gender bias Critical Criteria:

Drive Gender bias decisions and look for lots of ideas.

– What other jobs or tasks affect the performance of the steps in the Text Analytics process?

Predictive analytics Critical Criteria:

Transcribe Predictive analytics engagements and get going.

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

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

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

National Diet Library Critical Criteria:

Scrutinze National Diet Library risks and report on developing an effective National Diet Library strategy.

– In a project to restructure Text Analytics outcomes, which stakeholders would you involve?

– Do Text Analytics rules make a reasonable demand on a users capabilities?

News analytics Critical Criteria:

Brainstorm over News analytics strategies and diversify by understanding risks and leveraging News analytics.

– 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?

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

Plain text Critical Criteria:

Chat re Plain text risks and plan concise Plain text education.

– Are there Text Analytics Models?

Document Type Definition Critical Criteria:

Value Document Type Definition outcomes and oversee implementation of Document Type Definition.

Business intelligence Critical Criteria:

Analyze Business intelligence engagements and interpret which customers can’t participate in Business intelligence because they lack skills.

– When users are more fluid and guest access is a must, can you choose hardware-based licensing that is tailored to your exact configuration needs?

– Does the software provide fast query performance, either via its own fast in-memory software or by directly connecting to fast data stores?

– What statistics should one be familiar with for business intelligence and web analytics?

– Are we making progress? and are we making progress as Text Analytics leaders?

– What specialized bi knowledge does your business have that can be leveraged?

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

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

– What is your anticipated learning curve for Technical Administrators?

– How is Business Intelligence and Information Management related?

– What percentage of enterprise apps will be web based in 3 years?

– What BI functionality do we need, and what are we using today?

– What are the main full web business intelligence solutions?

– What is your anticipated learning curve for Report Users?

– What type and complexity of system administration roles?

– What are alternatives to building a data warehouse?

– Do we offer a good introduction to data warehouse?

– Why do we need business intelligence?

– Do you still need a data warehouse?

– Do you support video integration?

Concept mining Critical Criteria:

Learn from Concept mining adoptions and ask questions.

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

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

Copyright law of Japan Critical Criteria:

Deliberate Copyright law of Japan adoptions and display thorough understanding of the Copyright law of Japan process.

Conclusion:

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:

store.theartofservice.com/Text-Analytics-Complete-Self-Assessment/

Author: Gerard Blokdijk

CEO at The Art of Service | theartofservice.com

[email protected]

www.linkedin.com/in/gerardblokdijk

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:

Text analytics software| NICE LTD | NICE
www.nice.com/engage/customer-analytics/text-analytics

How to Use Text Analytics in Business – Data Informed
data-informed.com/how-to-use-text-analytics-in-business

Text Analytics — Blogs, Pictures, and more on WordPress
en.wordpress.com/tag/text-analytics

National Security External links:

National Security Division | Department of Justice
www.justice.gov/nsd

National Security Articles – Breitbart
www.breitbart.com/national-security

Market sentiment External links:

Market Sentiment – Investopedia
www.investopedia.com/terms/m/marketsentiment.asp

WhisperNumber.com / Market Sentiment LLC
www.whispernumber.com/signIn.jsp?source=whisper_profile.jsp

Earnings Whispers Market Sentiment
earningswhispers.com/marketsent

Fair use External links:

What is fair use? – Definition from WhatIs.com
whatis.techtarget.com/definition/fair-use

Stanford Copyright and Fair Use Center
fairuse.stanford.edu

Fair Use | Definition of Fair Use by Merriam-Webster
www.merriam-webster.com/dictionary/fair use

Record linkage External links:

“Record Linkage” by Stasha Ann Bown Larsen
scholarsarchive.byu.edu/etd/3833

Record linkage (eBook, 1946) [WorldCat.org]
www.worldcat.org/title/record-linkage/oclc/946122025

Freely Extensible Biomedical Record Linkage (FEBRL)
www.va.gov/TRM/ToolPage.aspx?tid=6427

Copyright Directive External links:

[PDF]Implementing the EU Copyright Directive
www.fipr.org/copyright/guide/eucd-guide.pdf

PubMed Central External links:

Need Images? Try PubMed Central | HSLS Update
info.hsls.pitt.edu/updatereport/?p=5136

PubMed Central (PMC) | NCBI Insights
ncbiinsights.ncbi.nlm.nih.gov/tag/pubmed-central-pmc

PubMed Central | Rutgers University Libraries
www.libraries.rutgers.edu/indexes/pubmed_central

Commercial software External links:

What is commercial software – Answers.com
www.answers.com/Q/What_is_commercial_software

E-file approved commercial software providers for …
www.tax.ny.gov/pit/efile/homepc.htm

efile with Commercial Software | Internal Revenue Service
www.irs.gov/e-file-providers/efile-with-commercial-software

Document processing External links:

LINGO – Web Based EDI Document Processing
www.myweborders.com

Document Processing Specialist Jobs, Employment | Indeed.com
www.indeed.com/q-Document-Processing-Specialist-jobs.html

Document Outsourcing | Document Processing | Novitex
www.novitex.com

Scientific discovery External links:

[PDF]Scientific Discovery and the Rate of Invention
www.nber.org/chapters/c2136.pdf

Scientific discovery (Book, 1990) [WorldCat.org]
www.worldcat.org/title/scientific-discovery/oclc/30872725

Most Popular “Scientific Discovery” Titles – IMDb
www.imdb.com/search/keyword?keywords=scientific-discovery

UC Berkeley School of Information External links:

[PDF]UC Berkeley School of Information
escholarship.org/uc/item/0fv601z8.pdf

UNIX Tutorial – UC Berkeley School of Information
people.ischool.berkeley.edu/~kevin/unix-tutorial/toc.html

UC Berkeley School of Information
www.ischool.berkeley.edu

Information extraction External links:

[PDF]Information Extraction – Brigham Young University
osm.cs.byu.edu/CS652s09/papers/Sarawagi.ieSurvey.pdf

[PDF]Title: Information Extraction from Muon …
math.lanl.gov/Research/Publications/Docs/borozdin-2004-information.pdf

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).

Structured data External links:

Introduction to Structured Data | Search | Google Developers
developers.google.com/search/docs/guides/intro-structured-data

C# HttpWebRequest with XML Structured Data – Stack Overflow
stackoverflow.com/questions/5653743

4 ways to improve SEO with schema and structured data
thenextweb.com/contributors/2017/06/02/4-ways-improv

Text Analytics External links:

How to Use Text Analytics in Business – Data Informed
data-informed.com/how-to-use-text-analytics-in-business

Text Analytics — Blogs, Pictures, and more on WordPress
en.wordpress.com/tag/text-analytics

Text analytics software| NICE LTD | NICE
www.nice.com/engage/customer-analytics/text-analytics

National Institutes of Health External links:

National Library of Medicine – National Institutes of Health
www.nlm.nih.gov

[PDF]NATIONAL INSTITUTES OF HEALTH
ethics.od.nih.gov/topics/OGE-450-Filing-Criteria.pdf

[PDF]NATIONAL INSTITUTES OF HEALTH
ccrhb.od.nih.gov/presentations/01132017_CCFocusGroups.pdf

Google Book Search Settlement Agreement External links:

Google Book Search Settlement Agreement – …
www.seomastering.com/wiki/Google_Book_Search_Settlement_Agreement

Topic 6 – The Google Book Search Settlement Agreement
www.wipo.int/meetings/en/doc_details.jsp?doc_id=122657

Social media External links:

Social Media | The Five | Fox News
www.foxnews.com/on-air/the-five/social

WhoDoYou – Local businesses recommended on social media
www.whodoyou.com

Text Analysis Portal for Research External links:

tapor.ca – TAPoR – Text Analysis Portal for Research
samesites.net/www/tapor.ca

tapor.ca – TAPoR – Text Analysis Portal for Research
minify.mobi/results/tapor.ca

tapor.ca : TAPoR – Text Analysis Portal for Research
www.pageviser.co/www/tapor.ca

Information visualization External links:

Information Visualization | SAGE Publications Inc
us.sagepub.com/en-us/nam/journal/information-visualization

Information visualization (Book, 2001) [WorldCat.org]
www.worldcat.org/title/information-visualization/oclc/44533836

Text mining External links:

Text Mining | Metadata | Portable Document Format
www.scribd.com/document/314423459/Text-Mining

Text Mining – AbeBooks
www.abebooks.com/book-search/title/text-mining

Text Mining, Semantics & Data Intelligence | SciBite
www.scibite.com

Data mining External links:

UT Data Mining
datamining.ogm.utah.gov

Data Mining (eBook, 2016) [WorldCat.org]
www.worldcat.org/title/data-mining/oclc/987749133

[PDF]Project Title: Data Mining to Improve Water Management
www.iitbmonash.org/Resources/Research_Opps_PDFs/IMURA_0261.pdf

Named entity recognition External links:

NAMED ENTITY RECOGNITION – Microsoft Corporation
www.freepatentsonline.com/y2015/0286629.html

Create an OpenNLP model for Named Entity Recognition …
gist.github.com/johnmiedema/4020deea875ce306971e

Ad serving External links:

AdGlare | AdServer Platform & Ad Serving Software 2017
www.adglare.com

ZEDO Ad Serving : Login
identity.zedo.com/oxauth/auth/mfa/login-otp

Powerful Ad Serving Simplified – AdButler
www.adbutler.com

Big data External links:

Event Hubs – Cloud big data solutions | Microsoft Azure
azure.microsoft.com/en-us/services/event-hubs

Pattern recognition External links:

Pattern Recognition – IMDb
www.imdb.com/title/tt0409138

Pattern Recognition – Official Site
www.journals.elsevier.com/pattern-recognition

Pattern recognition (Computer file, 2006) [WorldCat.org]
www.worldcat.org/title/pattern-recognition/oclc/845657880

Text categorization External links:

Text categorization – Scholarpedia
www.scholarpedia.org/article/Text_categorization

[PPT]Text Categorization With Support Vector Machines: …
www.ist.temple.edu/~vucetic/cis525spring2004/meghneel.ppt

[PDF]Title: Text Categorization for an Online Tendering …
www.cs.unb.ca/profs/hzhang/publications/baseweb2004.pdf

Semantic web External links:

As of 2015, is the semantic web dead? – Updated 2017 – Quora
www.quora.com/As-of-2015-is-the-semantic-web-dead

Semantic Web Company Home – Semantic Web Company
semantic-web.com

Sentiment Analysis External links:

dictionary – Sentiment Analysis Dictionaries – Stack Overflow
stackoverflow.com/questions/4188706

YUKKA Lab – Sentiment Analysis
www.yukkalab.com

Customer attrition External links:

Frustration = Customer Attrition | Mr. Shmooze
mrshmooze.com/2017/10/24/frustration-customer-attrition

Avoid Customer Attrition: Sell Tires! – Digital Dealer
www.digitaldealer.com/avoid-customer-attrition-sell-tires

What is customer attrition? | BigCommerce
www.bigcommerce.com/ecommerce-answers/what-customer-attrition

Intelligence analyst External links:

Military Intelligence Analyst Job Description (MOS 35F)
www.thebalance.com/35f-intelligence-analyst-3346147

So you want to be an intelligence analyst | Matthew Burton
matthewburton.org/become-an-intelligence-analyst

title:Intelligence Analyst Job Trends | Indeed.com
www.indeed.com/jobtrends/qt-intelligence-analyst.html

Spam filter External links:

Spam Filters Search Listings – Search for Spam Filters Info
Ad · alothome.com/spam-filters

How to Create an Outlook Junk Email or SPAM Filter
helpspot.business.uconn.edu/index.php?pg=kb.page&id=184

Visionary Communications – Spam Filter Login
spam.vcn.com

Corpus manager External links:

Virtual Corpus Manager – Archive of Department of …
www.computing.surrey.ac.uk/SystemQ/manual/vcm.html

Corpus manager – Revolvy
topics.revolvy.com/topic/Corpus manager

Security appliance External links:

Stratix 5950 Security Appliance – Allen-Bradley
ab.rockwellautomation.com › … › EtherNet/IP Network

European Commission External links:

European Commission (@EU_Commission) | Twitter
twitter.com/EU_Commission

REACH – Chemicals – Environment – European Commission
ec.europa.eu/environment/chemicals/reach/reach_en.htm

Customer relationship management External links:

Oracle – Siebel Customer Relationship Management
www.hpnquote.com

1workforce – Customer Relationship Management …
1workforce.com

Oracle – Siebel Customer Relationship Management
care.cummins.com

Internet news External links:

Technology News – New Technology, Internet News, …
phys.org/technology-news

Information retrieval External links:

Past Performance Information Retrieval System …
www.ppirssrng.csd.disa.mil

Introduction to Information Retrieval
www-nlp.stanford.edu/IR-book/

Information Retrieval – RMIT University
www1.rmit.edu.au/courses/004175

Competitive Intelligence External links:

Follow.net – Competitive Intelligence Software
app2.follow.net

Research Council External links:

Family Research Council – SourceWatch
www.sourcewatch.org/index.php/Family_Research_Council

North Dakota Oil and Gas Research Council
www.dmr.nd.gov/ogr

Family Research Council – frc.org
www.frc.org/advanced-search

Psychological profiling External links:

Pedophilia and Psychological Profiling
uspp.csbsju.edu/CrimPsych/CPSG-6.htm

Psychological Profiling Flashcards | Quizlet
quizlet.com/85046925/psychological-profiling-flash-cards

Part of speech tagging External links:

Part of speech tagging of Levantine [eScholarship]
escholarship.org/uc/item/0f62r9f8

[PDF]STUDY OF PART OF SPEECH TAGGING – ethesis
ethesis.nitrkl.ac.in/7240/1/Study_Ramesh_2015.pdf

Gender bias External links:

Most Popular “Gender Bias” Titles – IMDb
www.imdb.com/search/keyword?keywords=gender-bias

Free gender bias Essays and Papers – 123HelpMe
www.123helpme.com/search.asp?text=gender+bias

Title IX and Gender Bias in Language – CourseBB
www.coursebb.com/2017/01/23/title-ix-gender-bias-language

Predictive analytics External links:

Predictive Analytics for Healthcare | Forecast Health
www.forecasthealth.com

Inventory Optimization for Retail | Predictive Analytics
www.celect.com

Predictive Analytics Software, Social Listening | NewBrand
www.newbrandanalytics.com

National Diet Library External links:

National Diet Library | library, Tokyo, Japan | Britannica.com
www.britannica.com/topic/National-Diet-Library

Opening Hours & Library Holidays|National Diet Library
www.ndl.go.jp/en/service/tokyo/time.html

Online Gallery | National Diet Library
www.ndl.go.jp/en/gallery/index.html

Plain text External links:

GPS Visualizer: Convert GPS files to plain text or GPX
www.gpsvisualizer.com/convert

How to: Convert RTF to Plain Text (C# Programming Guide)
msdn.microsoft.com/en-us/library/cc488002(v=vs.120).aspx

Extracting Plain Text Data from NetCDF Files
www.narccap.ucar.edu/data/ascii-howto.html

Document Type Definition External links:

[PDF]Document Type Definition (DTD) – perfectxml.com
www.perfectxml.com/ph/xml-htp_06.pdf

[PDF]Document Type Definitions
homepage.cs.uiowa.edu/~slonnegr/xml/02.DTDs.pdf

Business intelligence External links:

Mortgage Business Intelligence Software :: Motivity Solutions
motivitysolutions.com

List of Business Intelligence Skills – The Balance
www.thebalance.com/business-intelligence-skills-2062364

Concept mining External links:

[PDF]Streaming Hierarchical Clustering for Concept Mining
uisacad5.uis.edu/~rloui2/pdfs/streaming_clustering.pdf

Concept mining – WOW.com
www.wow.com/wiki/Concept_Mining

Concept Mining using Conceptual Ontological Graph …
citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.100.3220

Copyright law of Japan External links:

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

[PDF]COPYRIGHT LAW OF JAPAN
www.wipo.int/edocs/lexdocs/laws/en/jp/jp035en.pdf

Copyright Law of Japan | e-Asia
library.uoregon.edu/easia/node/1460

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