Category Archives: Integrations

Posts about Meaningcloud’s integrations.

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.


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

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.

New MeaningCloud add-on for Google Sheets

Now you can use MeaningCloud’s text analytics without leaving your Google spreadsheet

New releaseDo you need to categorize verbatims of your customer surveys or analyze sentiment about your products on Twitter in the fastest and easiest way?

In that case, forget about learning new tools or having to program to connect to text analytics APIs.

The new MeaningCloud add-on for Google Sheets enables you to do semantic analysis from the comfort of your spreadsheet, without the need for programming or advanced analytical knowledge.

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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

You can now integrate MeaningCloud with all kinds of applications using Zapier

New releaseWould you like to integrate MeaningCloud with all kinds of applications?

Be able to categorize your customers’ emails and send notifications in Slack depending on their theme?

To extract comments from Twitter and generate a spreadsheet in Google Sheets with the sentiment associated with the brands mentioned?

Now you can quickly generate these processes thanks to the new MeaningCloud integration for Zapier.

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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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RapidMiner: Impact of topics on the sentiment of textual product reviews

This is the second of two tutorials where we will be using MeaningCloud Extension for RapidMiner to extract insights that combine structured data with unstructured text. Read the first one here. To follow these tutorials you will need to have RapidMiner Studio and our Extension for RapidMiner installed on your machine (learn how here).

In this RapidMiner tutorial we shall attempt to extract a rule set that will predict the positivity/negativity of a review based on MeaningCloud’s topics extraction feature as well as sentiment analysis.

To be more specific, we will try to give an answer to the following question:

  • Which topics have the most impact in a customer review and how do they affect the sentiment of the review that the user has provided?

For this purpose, we will use a dataset of food reviews that comes from Amazon. The dataset can be found here.

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Recorded webinar: Integrate the most advanced text analytics into your predictive models

Last April 27th we delivered our webinar “Integrate the most advanced text analytics into your predictive models”, where we presented our new MeaningCloud Extension for RapidMiner. Thank you all for your interest.

During the session we covered these items:

  • Analytics platforms. Introduction to RapidMiner.
  • Text analytics. Introduction to MeaningCloud.
  • Combining text and data analytics. MeaningCloud Extension for RapidMiner.
  • Practical case demo.
  • Application scenarios.
  • How this Extension is different.
  • Product roadmap.

IMPORTANT: The data analyzed during the webinar can be found in this tutorial, along  with the applied RapidMiner workflows and models.

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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RapidMiner: Relationship between product scores and text review sentiment

This is the first of two tutorials where we will be using MeaningCloud Extension for RapidMiner to extract insights that combine structured data with unstructured text. See the second one here. To follow these tutorials you will need to have RapidMiner Studio and our Extension for RapidMiner installed on your machine (learn how here).

In this tutorial we shall analyze a set of food reviews from Amazon. We will use the MeaningCloud sentiment API and try to see how users score products and whether their review description of a certain product corresponds to the score that they have assigned – more specifically we will try to see

  • How closely the review sentiment corresponds to the manually assigned score (which we already have available in our dataset).

The dataset that we will be using throughout the tutorial can be found here. First thing we need to do is download the CSV to our computer.

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