Category Archives: MeaningCloud

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

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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Communication during the Coronavirus (I): Thematic analysis in Spanish digital news media

While it is obvious that the priority during this pandemic is to cure the sick, to prevent new cases from surfacing and to ensure there are economic and social measures in place to help the people and businesses most afflicted overcome the current situation; without a doubt, in the near future, the analysis of content related to the coronavirus that has been generated by the media and social network users will be the object of research for numerous disciplines such as sociology, philology, linguistics, audio-visual communication, and politics, to name a few.

At MeaningCloud we want to do our bit in this area, by applying our experience and our Text Analytics solutions to analyze the enormous volume of information in natural language, in Spanish and in other languages, in Spain and in other countries, given that, unfortunately, this is a global crisis.

This first article in the series centers on the thematic analysis of content that has been generated in Spanish by digital media platforms in Spain over the last month, how it has evolved during this period of time and the informative positioning of the main media platforms in Spain.

These other articles (only available, at the moment, in Spanish) analyse conversation topics on Twitter in Spain (both from the hashtags and general topics perspective and also applying a specific thematic categorization) and the linguistic analysis of presidential speeches related to this crisis.

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Leverage deep text analytics to transform customer feedback into action (webinar)

Customer FeedbackOne of MeaningCloud’s goals is providing you with the best text analytics technology to help you better understand your customers and in recent times we have been launching products in this area: Voice of the Customer, Emotion Recognition, Intention Analysis.

But maybe you haven’t thought about how to use these products in a synergic way to analyze your customers’ feedback through surveys, contact center interactions and social media, and transform that feedback into action.

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COVID-19 Crisis: doing our part

As our CEO, Jose Gonzalez, announced a couple of days ago, in MeaningCloud, we believe that every little helps, and so we are adopting some measures to help our users and clients in these trying times.

Starting now:

  • We will provide full access to anyone participating in any of the NLP-related tasks published by platforms such as Kaggle to help in the research to fight or analyze the impact of COVID-19. Just contact us at support.
  • The Start-Up plan is doubled in volume: all current subscriptions, as well as subscriptions created while the COVID-19 crisis, persists will allow up to 240k credits per month.
fight COVID-19
  • The Start-Up plan now comes with a discount of $25 over its usual price for the next 3 months:
    • The discount has already been applied to all our current Start-Up subscriptions.
    • Any new subscription may benefit from this discount adding the coupon COVID-STARTUP in the upgrade process.
  • All our packs now cost $99 for the next 3 months. This discount has already been applied to current subscriptions to packs. New subscriptions should use the following coupons on checkout depending on the pack:

The upgrade process should include only the item the coupon applies to. These coupons are valid until May 31, 2020, or until the crisis ends.

Stay safe and take care of each other.

The MeaningCloud team


Fighting the coronavirus together – Message from the CEO

Fighting COVID-19

Dear friends,

In these challenging times, caused by the severe impact of the coronavirus, we have considered how we can best help our customers, friends and the communities our employees are a part of.

First of all, we believe that continuing our daily work and meeting commitments for our customers is our best contribution to the global economy and the recovery that should ensue. Digital companies must continue to contribute to the generation of wealth at a time like this. The loss of employment and vast public spending caused by the pandemic, with incalculable costs in health, social, and business protection, morally force us to contribute to sustaining the economy from our technological trench.

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RapidMiner + Python + MeaningCloud = 🚀

Integrations with third-party software are something extremely useful: they allow you to use technology outside the tool you are using, giving you additional features outside its core functionality or just providing auxiliary tools to make your day to day easier.

One of the downsides is that you are limited by the functionality the integration provides. Usually, this is not much of a problem as standard integrations tend to cover the most common use cases, but in the case of tools that can be used in many scenarios, these uses cases may not be exactly what you need or want for your application.

MeaningCloud is not an exception to this. We provide many different APIs, each one of them with several types of analyses and with tons of possible applications. It’s not surprising that not all of them are included in MeaningCloud’s extension for RapidMiner.

mc+python+rm

If you want something like the global polarity Sentiment Analysis provides, then the extension for RapidMiner has you covered, but it may not be the case for other analyses. It can go from wanting to use a MeaningCloud API not included in the extension such as the Summarization API or to something as small as needing the label of the resulting categories in an automatic classification process instead of the code the extension provides.

Last year, RapidMiner published a new Python scripting extension: Execute Python. This operator allows you to run a Python script in RapidMiner, which enables you to include any processing you want and can code in a Python script in your RapidMiner process.

Using this new functionality and MeaningCloud’s Python SDK, we can create a Python script to use any of MeaningCloud APIs directly from RapidMiner. The SDK enables us to work with the API output easily and to extract whatever information we want to add to our RapidMiner processes.

Let’s see how we can do this! Continue reading


Emotion Recognition in MeaningCloud

In our previous post, we made an introduction to emotion recognition to celebrate the release of the publication of our 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 emotion recognition pack. We will talk about the criteria and considerations we’ve followed in our approach.

Emotions

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