Category Archives: MeaningCloud

This category groups the different aspects of MeaningCloud we talk about in the blog.

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

Applying text analytics to financial compliance

In one of our previous posts we talked about Financial Compliance, FinTech and its relation to Text Analytics. We also showed the need for normalized facts for mining text in search of suspects of financial crimes and proposed the form SVO (subject, verb, object) to do so.

financial crime

Financial crime

Thus, we had defined clause as the string within the sentence capable to convey an autonomous fact. Finally, we had explained how to integrate with the Lemmatization, PoS and Parsing API in order to get a fully syntactic and semantic enriched JSON-formatted tree for input text, from which we will work extracting SVO clauses.

In this post, we are going to continue with the extraction process, seeing in detail how to work to extract those clauses from the response returned by the Parsing API.

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How to build a Financial Compliance model ready for FinTech

What is Financial Compliance and what is FinTech?

financial crime

Financial crime

Financial crime has increasingly become of concern to governments throughout the world. The emergence of vast regulatory environments furthered the degree of compliance expected even from other non-governmental organizations that conduct financial transactions with consumers, including credit card companies, banks, credit unions, payday loan companies, and mortgage companies.

Technology has helped financial services address the increased burden of compliance in innovative ways which have also yielded other benefits, including improved decision-making, better risk management, and an enhanced user experience for the consumer or investor.

The rapid development and employment of AI (Artificial Intelligence) techniques within this specific domain have the potential to transform the financial services industry.

FinTech (Financial Technology) solutions have recently arised as the new applications, processes, products, or business models in the financial services industry, composed of one or more complementary financial services and provided as an end-to-end process via the Internet. You can find additional interesting information in this article.

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Voice of the Employee Dashboard

Voice of the Employee gathers the needs, wishes, hopes, and preferences of all employees within an organization. The VoE takes into account both explicit needs, such as salaries, career, health, and retirement, as well as tacit needs such as job satisfaction and the respect of co-workers and supervisors. This post follows the line of Voice of the Customer in Excel: creating a dashboard. We are creating another dashboard, this time for the Voice of the Employee.

Text-based data sources are a key factor for any organization that wants to understand the “whys”.

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Recorded webinar: Vertical Packs, VoC, VoE

Thank you all for your interest in our webinar “MeaningCloud Vertical Packs: the Fastest Way to Benefit from Text Analytics” that we delivered last December 20th, where we explained how to customize text analytics with only one click  and we presented our  Packs for the analysis of the Voice of the Customer and the Voice of the Employee.

During the session we covered these items:

  • Introduction to text analytics and MeaningCloud.
  • Why Vertical Packs? How they create value.
  • What are the components of Vertical Packs: models, APIs, integrations.
  • Available Packs: Voice of the Customer, Voice of the Employee.
  • Case study: analysis of the Voice of the Customer.
  • Coming developments: product roadmap

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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Voice of the Customer in Excel: creating a dashboard

Excel spreadsheets are still one of the most extended ways of working with big collections of data, especially among non-technical users. Two of our Vertical Packs, Voice of the Customer and Voice of the Employee, are particularly useful for typically non-technical teams, which can now carry out their analyses easily with our last Excel integration.

In this tutorial, we are going to show you how to use the add-in provided in the Voice of the Customer Vertical Pack, how to carry out a VoC analysis, and how to work with its output by creating a dashboard like the one on the right. Working with the Voice of the Employee Pack would follow a similar pattern.

A practical case

Let us imagine we work for a market research department or agency interested in analyzing the Insurance industry. Customer comments in forums and social networks constitute an extremely valuable source of spontaneous information about their opinions about insurance providers.


We are going to focus specifically on auto insurance reviews extracted from ConsumerAffairs, a website that collects reviews from several domains.

The reviews we are going to use have been extracted from the top five companies in the Auto Insurance section: for each one of them we’ve picked ten items. You can download here the Excel spreadsheet we will be working on. It contains a single sheet where we have included two columns: one with the selected reviews, and another with the name of the company they refer to.

As we have mentioned, for this tutorial we are going to use our Vertical Pack for Voice of the Customer analysis. Vertical Packs are a combination of preconfigured models or dictionaries, powerful APIs and specific add-ins for Excel that enable you to adapt text analytics to your domain with only one click. Just by registering at MeaningCloud, you have a 30-day trial for all Vertical Packs available. The trial starts the moment you first analyze a text, so users that have been using MeaningCloud for a while will also be able to try it out.

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

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Accelerate text analytics’ time-to-benefit with our Vertical Packs

At MeaningCloud we have published our first Vertical Packs.

Our goal for them is to provide you with the fastest and least costly and risky way to make your text analytics initiatives profitable.

Preconfigured models and dictionaries

Usually one of the main costs of text analytics projects lies in building the models and dictionaries needed to adapt the tools to each application scenario, and at MeaningCloud we have always made it very easy thanks to the customization tools that the product includes.

But for those who do not have the resources to carry out this adaptation, the Vertical Packs give it to you already prepared for a set of scenarios. The Packs consist of a series of pre-prepared resources (dictionaries, deep categorization models, and sentiment models) focused on a series of typical scenarios (analysis of the Voice of the Customer, the Voice of the Employee, etc.) ready for immediate use and that provide analyses with an increased precision, recall, and relevance in these applications.

Use them from our add-ins for Excel

To make it easier to leverage the Vertical Packs, we have made them accessible through new add-ins for Excel, with support for the most useful operations, models, and analysis in each vertical.

Add-in for Excel

If you work for Marketing, Customer Support, or Human Resources and have thousands of comments from your customers or employees to analyze, sign up to MeaningCloud, download the corresponding add-in for Excel, paste your verbatims in a spreadsheet, press the relevant MeaningCloud button, and you will see how your comments are automatically tagged with meaningful categories for the analysis of the Voice of the Customer or the Employee.

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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!

Dockerized text analytics with MeaningCloud On-Premises

One of the main challenges users face when adopting an on-premises solution is the ability to integrate it into their infrastructure. The days when EJBs and application servers ruled the world have gone, and organizations bet for virtualization. They offer convenient features like isolation and replication, but along with a critical drawback: performance. Docker has raised as a serious alternative to virtual machines, and dockerized applications are the new EJBs. It is not uncommon to find in a company’s infrastructure dockerized services and processes. In this sense, a question rises: what about dockerized text analytics?

The problem with virtual machines

By definition, a virtual machine runs a complete stack of virtualized hardware and operating system. It takes a powerful host machine to run a large amount of virtual machines seamlessly. Organizations often find themselves forced to invest in powerful servers to run solutions that are in fact not specially hardware-demanding.

In the last years, an alternative approach called containers has been widely adopted. In short, a container is an isolated file system, with its own processes, users, and network interfaces, but without any virtualized hardware.

Dockerized text analytics with MeaningCloud

MeaningCloud runs seamlessly in Docker containers, which makes it a convenient solution for deploying it in some infrastructures. It also takes advantage of some appealing aspects inherited from the Docker internal design.

Text analytics: docker makes it easy

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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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