Tag Archives: deep categorization

Posts related to the Deep Categorization API

Automatic Emotion Recognition

In our previous post, we made an introduction to emotion recognition to celebrate the release of the publication of our automatic Emotion Recognition pack. We talked about how emotions play a prominent role in the individual and social life of people and how they have a great impact on their behavior and judgements.

We also saw how thanks to Natural Language Processing we can extract the underlying emotions expressed in a text in a fast and simple way and we saw how useful it can be in multiple scenarios.

In this post, we are going to explain in depth how to get the most out of our automatic emotion recognition pack. We will talk about the criteria and considerations we’ve followed in our approach.

Emotion Recognition

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Introduction to emotion recognition in text

Emotions govern our daily lives; they are a big part of the human experience, and inevitably they affect our decision-making. We tend to repeat actions that make us feel happy, but we avoid those that make us angry or sad.

Information spreads quickly via the Internet — a big part of it as text — and as we know, emotions tend to intensify if left undealt with.

Thanks to natural language processing, this subjective information can be extracted from written sources such as reviews, recommendations, publications on social media, transcribed conversations, etc., allowing us to understand the emotions expressed by the author of the text and therefore act accordingly.

Emotion

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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 NLP 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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Updated version of the IAB model in the Deep Categorization API

IAB - Interactive Advertising Bureau

The Interactive Advertising Bureau (IAB) is perhaps the most influential organization in the online advertising business and, currently, brings together more than 650 leading companies in the industry that control 86% of the U.S. market. With a strong presence in the rest of the industrialized world as well, today IAB has become a standard for content classification, especially in fields with strong ties to the digital economy and new social media.

In fact, IAB promotes advertising techniques like behavioral targeting, which allows advertisers to direct marketing campaigns to specific users (according to their age, place of residence, political views, interests, etc.) and thus increase their effectiveness. What’s more, the organization is making consistent progress in the field of geotargeting, an area of digital marketing that is on the rise thanks to the unprecedented diffusion of mobile devices connected to the Internet and the latest advances in Internet-of-things technologies. Continue reading →

MeaningCloud Release: new add-ins for Excel

In the last MeaningCloud release we presented our new Deep Categorization API, a new Premium API that gives us access to two of our new vertical packs: Voice of the Customer and Voice of the Employee.

We also know that many of the target users of these functionality may not be necessarily know how to code, so with that in mind, in this latest release we are publishing two new add-ins, one for each vertical pack:

Both add-ins provide an integration with the Deep Categorization API, but focus on giving a more user-friendly approach for the analysis each one of them provides.

MeaningCloud release

The add-ins are adapted so anyone can obtain the analysis they want with just a few clicks, without worrying about API parameters or leaving the environment where they have the data to analyze.

This release also contains minor security updates as well as bug fixes in our core engines.

If you have any questions or just want to talk to us, we are always available at support@meaningcloud.com!

MeaningCloud Release: new Deep Categorization API

This is what we’ve included in MeaningCloud’s latest release:

  • New Deep Categorization API: we are happy to present the first of our Premium APIs, Deep Categorization 1.0, which lets you carry out an in-depth categorization of your data. In this initial release, we’ve included predefined models for analyzing the Voice of the Customer in several domains and the Voice of the Employee.
  • Language Identification 1.1: we say goodbye to Language Identification 1.0, so if you are still using it, you will need to migrate to the newest version. If you are using it through the Excel add-in, we’ve done it for you, so you just have to update your Excel add-in to the latest version.
  • New language for Text Clustering: we’ve added Catalan to the languages supported in the Text Clustering API.
  • General usability improvements: mainly in the developer area of the website.
New NeaningCloud release

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