Category Archives: MeaningCloud

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

MeaningCloud Europe is now part of Reddit

Reddit & MeaningCloud

I’m thrilled to announce that MeaningCloud Europe (formerly Daedalus), the company I started in 1998, is now a part of Reddit. You can read the full announcement of this acquisition on Reddit’s blog.

You all know Reddit, of course, the online platform hosting more than 100,000 communities where people can discuss and chat about their interests, hobbies, and passions.

And you know MeaningCloud, the Language Technology company that competes (modestly but proudly) with IBM Watson, Google Cloud, Amazon Comprehend, and Microsoft Cognitive Services. Some of you may remember that we started offering public text analytics SaaS services under the name Textalytics in 2013, more or less simultaneously with the commercial launch of IBM Watson APIs.

As geeks, working on Content Understanding as part of the fantastic Reddit’s machine learning (ML) team, around the largest internet corpus of conversational content, is a dream come true. There is no need to explain the excitement of our team about the new challenges we face as part of Reddit.

At this point, some of you, users, clients, or friends who follow us, may be wondering what happens now with our current businesses. I’m happy to announce that, as part of the deal, we have transferred our clients to our shareholder Sngular. Sngular (Singular People, S.A.) is a global provider of IT services, a Spanish public company with 1,300 employees. At MeaningCloud, we are proud to have participated as founding partners of Sngular in 2015. In particular, Sngular is taking over:


  • Our wholly-owned subsidiaries, MeaningCloud LLC and Konplik Health Inc. The first one will keep running our text analytics platform and servicing our clients and users worldwide (including 60,000 users of our free tier). Konplik will continue providing AI services to the health and pharma industry.
  • Our stake at Expoune Inc., a services company built around exploiting MeaningCloud’s technology in different settings.
  • Stilus, our automatic spell, grammar, and style assistant for Spanish, with 250,000 registered users, the Spanish checker preferred by media companies and professionals (translators, copyeditors, journalists, and lovers of the Spanish language).
  • Our clients in different industries (consulting, finance, infrastructure, telecommunications, etc.) for which we develop specific embedded solutions (SaaS or on-prem) on top of our technology.

So, we are leaving our business in the best hands, under the direction of Cesar Camargo (Sngular CEO), Alma Miller (CEO of Sngular in the United States), and José Luis Calvo (head of Artificial Intelligence). It’s your baby now!

You may have your preferred case among pivoting companies: Netflix, IBM, Western Union, Android, Nintendo… They made substantial changes in their core activities or strategy over the years. What about us? Competing in hi-tech is (extremely) tough. Starting an Artificial Intelligence company in Spain in 1998 was evidence of madness (shared with my colleagues Juan R. Velasco and Luis Magdalena). And taking it to here (along with Antonio Laorden, José L. Martínez and Julio Villena) was against any odds. On the way, we pivoted our business several times. For example, in the last seven years, we took part in the birth of Sngular (following the vision of Jose Luis Vallejo), created two new US-based companies, and took a stake in Expoune (led by Robert Wescott).

On the technology side, however, we have followed a much more consistent path based on three principles: technological agnosticism, problem-solving mindset, and client orientation. For example, Stilus was our first Natural Language Processing (NLP) product. The newspaper El País was our first corporate client in the news industry in 2004. And, 18 years later, we keep maintaining Stilus, under the same license contract, despite the changes in technology, product, people, and content platforms.

Blog Reddit

Today, we pivot again to be part of a large and fast-growing company in the social media arena. And we face this challenge with all the experience and assets accumulated over the years. Time is the best judge of our actions. However, I’m sure about the great value that we will be contributing to Reddit in the coming years, configuring one of the leading organizations in the exploitation of NLP  technologies, beyond the current hype around ML and Artificial Intelligence (AI) in general.

For me, all this means turning over a new leaf. Having got my Ph.D. in AI in the late 80s, 30 years as a university professor, 24 years since I started up my first company… I meet all the requirements to be the senior of “intern snoos” at Reddit. I’m committed to doing my part, side by side with my incredible team, under the direction of Jack Hanlon and Rosa Català, to contribute, in my poor power, to Reddit’s growth and international expansion. It sounds like a plan, right?

Thank you, and good luck to all the people (past employees, investors, clients, users, and friends) who have supported us along the road. And let’s meet on Reddit!

All the best,

Jose C. Gonzalez


Machine Learning for NLP/Text Analytics, beyond Machine Learning

In the field of text analytics, aside from the development of categorization models, the application of machine learning (and more specifically, deep learning) has proved to be very helpful for supporting our teams in the process of building/improving rule-based models.

This post analyzes some of the applications of machine/deep learning for NLP tasks, beyond machine/deep learning itself, that are used to approach different scenarios in projects for our customers.

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Accuracy measures in Sentiment Analysis: the Precision of MeaningCloud’s Technology

Accuracy Measures of Commercial Sentiment Analysis APIs

Our clients frequently ask, “what’s the precision of MeaningCloud technology?” How does it compare with other commercial competitors and with state-of-the-art technology? And they demand precise numbers.

That’s not an easy question to answer. Even when there are milliards of research studies on this issue. For the sake of simplicity, let’s concentrate on the well-studied scenario of accuracy measures in Sentiment Analysis. Continue reading

New Excel 365 add-in for Text Analytics!

Our new Excel 365 add-in has finally arrived!

Excel is the preferred tool for many MeaningCloud users. They access MeaningCloud APIs directly from Excel with our add-in. In the last months, we have received a lot of inquiries about Mac support. So, we partnered with Microsoft to build a new multiplatform version.



Installing it is a breeze on all platforms. The new add-in is available in Microsoft AppSource:

Click on Get it Now and follow the instructions.



You only need your API key to use MeaningCloud. Paste it in the License Key field and you’re ready to start analyzing.

Don’t have one? Create an account for free – no payment method required.

Configuring the MeaningCloud add-in



You can use the APIs directly from the ribbon:

MeaningCloud add-in ribbon

The user interface page describes the different buttons. Paste your texts in the spreadsheet, select the tool in the ribbon, review the parameters and click in Analyze:
Sentiment analysis with MeaningCloud
Take a look at the documentation for more information about add-in usage.


But I don’t use Office 365!

No worries. If you use another Excel version, we still offer the previous add-in version. If you don’t use Microsoft Excel at all, you can use our Google Spreadsheets add-on.



If you have any questions or issues, we will be glad to hear from you. Drop us a line at and tell us about your experience.

Language Technology vs. Disinformation

Reuters Institute Digital News Report 2020

Following the 2016 US presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. Research from Oxford University’s Reuters Institute for the Study of Journalism has found a long and steady decline in trust in traditional media. (See Reuters Institute Digital News Report 2020). This declining trust coincides with the uprise of social media as a main source of information. In 2020, social media was a source of news for 48 percent of the public, up from 27 percent in 2013, according to the Reuters Institute.

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IAB Taxonomy Level 3 now available in our Deep Categorization API

IAB - Interactive Advertising BureauDigital marketing is becoming a fundamental pillar, by leaps and bounds, in the business plans of practically every business model. Methods are being refined and the search for the connection between brand and user is expected to become increasingly more precise: a related advertisement is no longer sufficient, now the advertisement must appear at the right time and in the right place. This is where categorization proves to be an exceedingly useful tool.

That is why, at MeaningCloud, we have improved our IAB categorization model in English, that is integrated in our Deep Categorization API:

  • Adding a third level of content taxonomy to the hierarchy of categories (IAB Taxonomy Level 3).
  • Improving the precision of pre-existing categories.
  • Including the unique identifiers defined by IAB itself for each of the categories.

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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

Obtain deep customer insights with MeaningCloud

Companies need to analyze the feedback that customers provide to them through a variety of unstructured channels: surveys, interviews, contact center, social media. However, the text analytics solutions available are limited to a shallow analysis of the feedback. In this post we show you how to use deep analytics to get a complete picture of customer opinions, perceptions, emotions and intentions.

Companies need to become customer-focused in order to understand the needs and opinions of their customers and thus, define the proverbial “segment of 1”. This forces us to implement Voice of Customer (VoC) analysis initiatives that go far beyond the typical periodic satisfaction survey with numerical scores, to look for new sources of insights.

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Recorded webinar: Deep text analytics to transform customer feedback into action

Last April 29th we delivered our webinar “Leverage deep text analytics to transform customer feedback into action”. Thank you all for your interest.

In it we explained how to use Meaning Cloud’s products in a synergic way to analyze your customer feedback through surveys, contact center interactions and social media, and level up your customer insights.

During the session we covered these items:

  • Leveraging unstructured customer feedback: benefits and challenges
  • Text analytics to the rescue… but with limitations
  • How to use deep text analytics to extract more actionable insights
    • Pre-made Insights
    • Adaptation
    • Development
  • understand the opinions, perceptions, emotions and intentions of your customers.

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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