Text analytics and Natural Language Processing took center stage in the latest Statistically Speaking webinar What Value Will Your Text Data Reveal. Nearly 700 registrants, including more than 400 new contacts to Salesforce, enjoyed NLP expert and consultant Manya Mayes’ keynote outlining the benefits of text analytics, best practices and how to communicate insights gained from text data. Manya was then joined by Jeff Swartzel, statistician for Procter & Gamble and Weida Tong from the FDA.
To help JMP sales team engage in more meaningful and productive post-event conversations with customers and prospects, we’ve created the following list of recommended resources to position now that the event has concluded. This list pulls appropriate assets from across our web site.
If you have any questions about recommendations related to this event, please feel free to contact me.
Larry - x11276 in Cary.
Salesforce Campaign Link Found Here
For starters, consider sending the link to the New in JMP 16 page. Term selection and sentiment analysis are new features in JMP 16 that we highlight on this page. We also created a short video, Term Selection and Sentiment Analysis, you can find on YouTube. It’s a slick video that hits the highlights of these new features in Text Explorer.
Empowering P&G Employees to do text analysis, JMP Foreword article
This article is from P&G’s Scott Reese, a colleague of panelist Jeff Swartzel. Jeff references the article during his talk – it’s a great case study in moving an organization toward text analytics.
Sentiment Analysis Comes to JMP Pro 16, JMP Blog
This blog from Ross Metusalem takes a deep dive into JMP Pro’s sentiment analysis, now available in JMP 16. Sentiment analysis enables you to gauge how positively or negatively people feel about something, based on their written comments. Ross uses consumer sentiment with coffee pods to show how JMP handles written feedback. He shows how analysis should always be tailored to the specific context, and how JMP includes a variety of features to easily customize the sentiment dictionary and scoring system as you explore the data. It’s the most recent asset we have on the subject.
Unlocking the Value of Text Analytics, Analytically Speaking
This webinar with Seth Grimes discusses shares the fact that nearly 80 percent of business-relevant information is in unstructured formats. Grimes gives his perspective on unlocking the value in unstructured text data, best practices in natural language processing and sentiment analysis technology.
Tackling Unstructured Data with Text Exploration, Technically Speaking
Though a little older, this Technically Speaking talks about the variety of unstructured data, things like surveys, product complaints, maintenance reports, customer feedback, etc., and how text exploration capabilities can help you quickly extract insights from text-based data. It highlights some earlier versions of JMP software, including word clouds, which allows you to visualize word frequency in a way that makes clear the prevailing themes in any given unstructured data set. Also shows how to manipulate the importance placed on each term in the matrix and explore the themes that are most relevant to your organization using topic extraction.
Tab into unstructured data, White Paper
This white paper is older, but is still our primary white paper on text analytics. The paper explores various capabilities in JMP® and JMP Pro Text Explorer and how they can help unlock text-based insights. To demonstrate the process, author Heath Rushing draws on an enlightening example from a collection of National Science Foundation research grant abstracts.
Using Text Explorer to Extend Analysis, Mastering JMP Webinar
This Mastering shows how to use Text Explorer to include analyses of unstructured text, such as comment fields in surveys or incident reports. See, in this consumer study, how to use unstructured data to more fully understand the results of a local government business-related survey. The webinar covers navigating Text Explorer, identifying terms and phrases, mapping results and more.
You might also want to consider positioning our Statistical Thinking for Industrial Problem Solving, free online course. This whole course is seven modules, totaling about 30 hours of self-paced learning. All are excellent but consider highlighting the Predictive Modeling and Text Mining module. It covers many of the same topics covered in the seminar including how to extract information and meaning from unstructured text data, such as survey response data.
Thanks Larry - these are great!
