Tag Archives: text classification

Posts related to automatic text classification

Text Classification 2.0: Migration Guide

We’ve recently published a new version of our Text Classification API, which comes hand in hand with a new version of the Classification Models Customization console.

In both these new versions, the main focus is on user models. We know how important it is to easily define the exact criteria you need, so the new classification API supports a new type of resource, the one generated by the Classification Model Customization Console 2.0.

In this post, we will talk about how to migrate to these new versions if you are currently using the old ones. Text Classification 1.1 and Classification Models 1.0 will be retired on 15/Sep/2020. Continue reading →

New Release: Text Classification 2.0

We’re happy to announce we have just published a new version of our Text Classification API, which comes hand in hand with a new version of the Classification Models Customization console.

In both these new versions, the main focus is on user-defined models. We know how important it is to easily define the exact criteria you need, so the new classification API supports a new type of resource, the one generated with the Classification Models Customization console 2.0.

With these new versions, we’ve aimed to:

  • Make criteria definition easier: more user-friendly operators to improve overall rule readability, and new operators to provide more flexibility.
  • Remove dependencies between categories in a model that made their maintenance and evolution cumbersome.
  • Give the user more control over where the relevance assigned to the categories comes from.
MeaningCloud release

Let’s see with a little more detail what’s new. Continue reading →

Case Study: Text Analytics against Fake News

Everybody has heard about fake news. Fake news is a neologism that can be formally defined as a type of yellow journalism or propaganda that consists of deliberate disinformation or hoaxes spread via traditional print and broadcast news media or online social media. It is also commonly used to refer to fabricated or junk news, with no basis in fact, but presented as being factually accurate.

The reason for putting someone’s efforts in creating fake news is mainly to cause financial, political or reputational damage to people, companies or organizations, using sensationalist, dishonest, or outright fabricated headlines to increase readership and dissemination among readers using viralization. In addition, clickbait stories, a special type of fake news, earn direct advertising revenue from this activity.

Continue reading →

Liberty Shared: how an NGO uses Text Analytics

Liberty Shared[EDITOR’S NOTE: This is a guest post by Xinyi Duan, Director of Technology and Data Research at Liberty Shared.]

Liberty Shared is committed to ensuring that the experiences of vulnerable and exploited workers around the world is represented in our markets, legal systems, and information infrastructures. To do this, we have to take on the daunting task of wrangling some of the messiest data that have been previously un-mined and unstructured.

MeaningCloud has enabled us to quickly and effectively deploy NLP techniques to tackle these problems, and it works easily for team members who are using NLP statistical models already to those without that technical background. It is also powerful enough to grow with our programs. As we learn more about the problem, it is easy to update the models to reflect our learnings.

Continue reading →

Easy Text Analytics using MeaningCloud’s Zapier integration

We at MeaningCloud love Zapier. It lets us build workflows connecting email, Slack, etc. We wanted to contribute our bit to its ecosystem, so we created MeaningCloud’s Zapier integration. Thanks to it, we can perform Text Analytics in any Zapier workflow easily.

Many organizations use workflows to automate tasks. Chat rooms and bots are a common way of triggering events. For instance, the Slash commands in Slack or Hubot respond to well-formed commands with strict patterns to avoid ambiguity, which is something desirable under some circumstances.

Zapier logo

Where these approaches do not fit specially well is, precisely, one of the most exciting aspects of using Text Analytics in automatization: it can react to the outside world. A company can analyze all communications received from clients, measure reputation, detect weaknesses, or even analyze the employee satisfaction. And all that information can be injected in an automated process and react conveniently.

In this article, we will learn how to integrate MeaningCloud in any Zapier workflow. Continue reading →

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 →

What is the Voice of the Employee (VoE)?

Voice of the Employee. Silhouettes with bubbles representing dialog

Finding committed employees is one of public and private organizations’ top priorities. Thus, listening to the Voice of the Employee by systematically collecting, managing and acting on the employee feedback on a variety of valuable topics is essential.

The relationship between Voice of the Employee (VoE) and Engagement is very similar to the one between Voice of the Customer (VoC) and Customer Experience. VoC provides information to improve customer experience. Voice of the Employee promotes employees’ engagement in the company and their work. See: Voice of the Employee, Voice of Customer and NPS

Voice of the Employee collects the needs, wishes, hopes, and preferences of the employees of a given company. VoE considers specific needs, such as salaries, career, health, and retirement, as well as implicit requirements to satisfy the employee and gain the respect of colleagues and managers.
Continue reading →

Text Classification in Excel: build your own model

Customized Text Classification for Excel

In the previous tutorial we published about Text Classification and MeaningCloud’s Excel add-in, we showed you step by step how to carry out an automatic text classification using an example spreadsheet.

In this tutorial, we are going a bit further: instead of just using one of the predefined classification models we provide, we are going to create our own model using the model customization console in order to classify according to whichever categories we want.

We are going to work with the same example as before: London restaurants reviews extracted from Yelp. We will use some data from the previous tutorial, but for this one we need more texts, so we’ve added some. You can download the spreadsheet here if you want to follow the tutorial along.

If you followed the previous tutorial, you might remember that we tried to use the IAB model (a predefined model for contextual advertisement) to classify the different restaurant reviews and find out what type of restaurants they were. We had limited success: we did obtain a restaurant type for some of them, but for the rest we just got a general category, “Food & Drink“, which didn’t tell us anything new.

This is where our customization tools come in. Our classification models customization console allows you to create a model with the categories you want and lets you define exactly the criteria to use in the classification.

So how do we create this user model?
Continue reading →

Text Classification in Excel: getting started

As you probably already know, Excel spreadsheets are one of the most extended ways of working with big collections of data. They are powerful and easy to combine and integrate with a myriad of other tools. Through our Excel Add-in, we enable you to add MeaningCloud’s analysis capabilities to your work pipeline. The process is very simple as you do not need to write any code.

In this tutorial, we are going to show you how to use our Excel Add-in to perform text classification. We are going to do so by analyzing restaurant reviews we’ve extracted from Yelp. If you have already read some of our previous tutorials, this first part may sound familiar.

To get started, you need to register in 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.

Once you’ve installed it, a new tab called MeaningCloud will appear when you open Excel. If you click on it, you will see the following buttons:

excel add-in ribbon

To start using the add-in, you need to copy your license key and paste it into the corresponding field in the Settings menu. You are required to do this only the first time you use the add-in, so if you have already used it, you can skip this step.

Once the license key is saved, you are ready to start analyzing!
Continue reading →