Recorded webinar: Solve the most wicked text categorization problems

Thank you all for your interest in our webinar “A new tool for solving wicked text categorization problems” that we delivered last June 19th, where we explained how to use our Deep Categorization customization tool to cope with text classification scenarios where traditional machine learning technologies present limitations.

During the session we covered these items:

  • Developing categorization models in the real world
  • Categorization based on pure machine learning
  • Deep Categorization API. Pre-defined models and vertical packs
  • The new Deep Categorization Customization Tool. Semantic rule language
  • Case Study: development of a categorization model
  • Deep Categorization – Text Classification. When to use one or the other
  • Agile model development process. Combination with machine learning

IMPORTANT: this article is a tutorial based on the demonstration that we delived and that includes the data to analyze and the results of the analysis.

Interested? Here you have the presentation and the recording of the webinar.

(También presentamos este webinar en español. Tenéis la grabación aquí.)
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Tutorial: create your own deep categorization model

As you have probably know by now if you follow us, we’ve recently released our new customization console for deep categorization models.

Deep Categorization models are the resource we use in our Deep Categorization API. This API combines the morphosyntactic and semantic information we obtain from our core engines (which includes sentiment analysis as well as resource customization) with a flexible rule language that’s both powerful and easy to understand. This enables us to carry out accurate categorization in scenarios where reaching a high level of linguistic precision is key to obtain good results.

In this tutorial, we are going to show you how to create our own model using the customization console: we will define a model that suits our needs and we will see how we can reflect the criteria we want to through the rule language available.

The scenario we have selected is a very common one: support ticketing categorization. We have extracted (anonymized) tickets from our own support ticketing system and we are going to create a model to automatically categorize them. As we have done in other tutorials, we are going to use our Excel add-in to quickly analyze our texts. You can download the spreadsheet here if you want to follow the tutorial along. If you don’t use Microsoft Excel, you can use the Google Sheets add-on.

The spreadsheet contains two sheets with two different data sets, the first one with 30 entries, the second one with 20. For each data set, we have included an ID, the subject and the description of the ticket, and then a manual tagging of the category it should be categorized into. We’ve also added an additional column that concatenates the subject and the description, as we will use both fields combined in the analysis.

To get started, you need to register at MeaningCloud (if you haven’t already), and download and install the Excel add-in on your computer. Here you can read a detailed step by step guide to the process. Let’s get started! Continue reading


The leading role of Natural Language Processing in Robotic Process Automation

RPA

Robotic Process Automation

Robotic Process Automation is gaining traction

Robotic Process Automation (RPA) has attracted considerable attention as a way to automate repetitive clerical tasks, by mimicking the way human workers carry them out. Since the introduction of the term (around the year 2000), RPA has evolved from simple screen scraping and desktop automation to the promise of Cognitive RPA. Reports by industry analysis leaders estimate the global spending on RPA software to reach $2.4B in 2022, with annual growth rates over 50%.

While the RoI of these investments is quite apparent, most analysts also stress that automation does not necessarily imply intelligence. In a recent article published by Forbes (“Sorry, but your bots are stupid”), Ron Schmelzer stresses the fact that automation is inherently dumb, and that automated software bots are still dumb. Concluding that “despite much of the marketing hype, what is being sold as intelligent automation is far from intelligent.”

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New Release: Deep Categorization Customization Console

One of the APIs that has had more “movement” lately in our updates is the Deep Categorization API, which — as many of you already know — provides an easier, more flexible and precise way to categorize texts. Most of this movement has come in the form of new supported models such as Intention Analysis, as well as many under-the-hood improvements.

We are happy to announce that we have finally released the Deep Categorization customization console in our web.

This console will allow you to create accurate models for those scenarios where you need a very high level of linguistic precision to differentiate between the different categories you want to detect.

MeaningCloud release

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Case study on the voice of the patient for the Pharma industry

Pharmaceutical companies are extending their Voice of the Patient projects to include social media: comments on web forums, surveys, Twitter, and more.

The goal of the proof of concept ordered by one particular pharmaceutical company in Spain was to: ” Collect and analyze the voice of the patient, both quantitatively and qualitatively, from the channels where it is expressed”, including social networks like web forums, Facebook, Twitter, and other systems.

For the pharma industry, it is essential to listen and understand the feedback that their current and potential customers communicate through various means and touchpoints.

Web forums, for instance, gather millions of posts, and function as a meeting point for patients where support, experiences, and wisdom are shared with peers, family members, and friends.

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Web scraping and text analytics

Text analytics projects are often dependent on Internet-based public sources such as the World Wide Web. These projects usually begin by extracting data from a variety of websites. We call this process “web scraping” (or “web harvesting”). While users can handle web scraping manually, the term often refers to automated methods executed utilizing a web crawler.

Examples of projects that offer a valuable wealth of information include customer experience (in the same way as patient experience or employee experience), dynamic pricing and revenue optimization, competitor monitoring, or compliance checking. Continue reading


People Analytics: MeaningCloud book on Amazon!

People Analytics. Data and Text Analytics for Human Resources

People Analytics. Data and Text Analytics for Human Resources. This MeaningCloud book is available on Amazon.

In People Analytics, and in this book, we use the evidence that the data provides to respond to several questions:

  • Which candidate will be high-performing, effective, loyal, and aligned with the corporate culture?
  • How can we measure the economic impact of a training program?
  • How can I segment the workforce to make their actions more effective?
  • Which people are considering leaving the organization?
  • What net benefit will employees contribute throughout time in a particular position?
  • How does employee commitment affect productivity and economic outcomes?
  • How can I design a study that is statistically and mathematically valid?

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MeaningCloud Release: Sentiment + Nordic Pack

Not long ago we published the first of our Language Packs: the Nordic pack, which includes several text analytics tasks in Swedish, Danish, Norwegian and Finnish.

Among the text analytics tasks supported, there’s one that was missed by many of you: Sentiment Analysis API. Well, no more!

We are happy to announce that from now on you can also analyze sentiment in the four languages included in the Nordic pack. And what’s more, for those of you that are already subscribed to the pack, it has been automatically included and so you can start using it right away without any change in pricing.

MeaningCloud release

For those of you that are not subscribed to the Nordic pack, remember that you can test all our packs full functionality by requesting a 30 day period trial. It’s super easy!

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