Category Archives: APIs

Posts about Meaningcloud’s APIs.

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


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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Text Analytics for the Contact Center of the Future

The contact center is a crucial component of the customer experience and increasingly incorporates more channels based on unstructured information. In this post we analyze how advanced semantic analysis can be used to get the most out of the contact center of the future.

The Rise of the New Contact Center

Interest in the contact center has multiplied by its greater role as an essential component of the customer experience. New interaction channels (bots, chats, social) add to the traditional email and telephone and enable innovative ways to connect with clients in both inbound and outbound contact centers, both internal to companies of all types and in those operated by BPO vendors to provide outsourced services.

In this way, the contact center (traditionally known as call center) has ceased to be a cost center to become a tool for proactively communicating with and understanding the market, for multichannel business development and for generating value for the company.
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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.

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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.

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Invoking the MeaningCloud Sentiment Analysis API from Minsait’s Onesait Platform

Minsait’s Onesait Platform is an IoT & Big Data Platform designed to facilitate and accelerate the construction of new systems and digital solutions and thus achieve the transformation and disruption of business. Minsait is a brand of Indra: its business unit addressing the challenges posed by digital transformation to companies and institutions.

Minsait has published a post about the procedure to invoke an external API from the integrated flow engine of the Onesait Platform (formerly known as Sofia2).

MeaningCloud integrated with Minsait Onesait Platform

The post titled HOW TO INVOKE AN EXTERNAL REST API FROM THE SOFIA2 FLOW ENGINE? uses as an example the integration of MeaningCloud Sentiment Analysis API (in Spanish).

The article illustrates one of the strengths of MeaningCloud: how easy it is to integrate its APIs into any system or process.


Recorded webinar: Solve the most wicked text categorization problems

Thank you all for your interest in our webinar “A new tool for solving wicked text categorization problems” that we delivered last June 19th, where we explained how to use our Deep Categorization customization tool to cope with text classification scenarios where traditional machine learning technologies present limitations.

During the session we covered these items:

  • Developing categorization models in the real world
  • Categorization based on pure machine learning
  • Deep Categorization API. Pre-defined models and vertical packs
  • The new Deep Categorization Customization Tool. Semantic rule language
  • Case Study: development of a categorization model
  • Deep Categorization – Text Classification. When to use one or the other
  • Agile model development process. Combination with machine learning

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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Case study on the voice of the patient for the Pharma industry

Pharmaceutical companies are extending their Voice of the Patient projects to include social media: comments on web forums, surveys, Twitter, and more.

The goal of the proof of concept ordered by one particular pharmaceutical company in Spain was to: ” Collect and analyze the voice of the patient, both quantitatively and qualitatively, from the channels where it is expressed”, including social networks like web forums, Facebook, Twitter, and other systems.

For the pharma industry, it is essential to listen and understand the feedback that their current and potential customers communicate through various means and touchpoints.

Web forums, for instance, gather millions of posts, and function as a meeting point for patients where support, experiences, and wisdom are shared with peers, family members, and friends.

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Text analytics explained: MeaningCloud in Italian

In previous posts we spoke about text analysis performed in French and Portuguese. Today we’re wrapping up this linguistics series by discussing the analyses that can be done with Italian texts.

Italian is spoken in several European countries such as Italy, San Marino and Switzerland, totaling almost 70 million speakers. As Italians have migrated all over the world, its language is also present on the other side of the pond. In South America, for instance, it is the second most spoken language in Argentina. In the US, even though it is not an officially spoken language, many of its citizens are of Italian descendent and thus speak the language at home. We wanted to include such a widely spread language in our Standard Languages Pack.

Hello in many languages

Similarly to our previous posts, we are going to explain, in a linguistically-inclined way, what Text Analytics is and which functionalities MeaningCloud provides in Italian.

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