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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Applications of Text Analytics in the Tourism Industry

Understand Your Visitors, Improve Your Offering

Tourism is one of the largest economic activities, with statistics indicating that people spend more discretionary income on travel than on home improvement, financial investment, or even health.

But how people travel is changing. For example, people are spending more and more time researching trip details on their mobile devices. In 2016, 40% of US travel site visits and 60% of searches for destination information came from mobile devices, and travelers are increasingly consuming and publishing information on tourism in online travel agencies, social networks, or review sites such as TripAdvisor,, etc.

A new generation of contextual semantic analysis applications allow us to leverage all that information and communicate more naturally with hyperconnected tourists. These applications range from analyzing comments on social media to understanding natural language which allows us to develop much more conversational assistants and bots.

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Our experience on Adverse Drug Reactions (ADR) identification at TAC2017

MeaningCloud and LaBDA research group were present at the TAC 2017 conference held on November 13th – 14th at NIST headquarters in Washington. In the Text Analysis Conferences, research groups from all over the world were invited to develop software systems to tackle text analytics-related problems. This year, one task was devoted to the automatic identification of adverse drug reactions (ADRs) appearing in drug labels, including features defining the ADR, such as its severity or if it is characteristic of a drug class instead of just a given drug. There has been a specific subtask to link the identified ADRs with their corresponding MedDRA codes and lexical terms. More than 10 research teams have taken part in the project, all of them applying some kind of deep learning approach to the problem. Results show that it is possible to reach 85% accuracy when identifying ADRs.

We were delighted to present our text analytics-based system for ADRs identification on drug labels, which combines natural language processing and machine learning algorithms. The system has been built as a joint effort between MeaningCloud and LaBDA research group at the Universidad Carlos III de Madrid. Identifying ADRs is a basic task for pharmacovigilance, and that is the reason why the Federal Drug Administration (FDA) is involved in the funding and definition of the ADRs identification tasks in the framework of the Text Analysis Conferences. We have learned a lot these days (e.g., a BiLSTM deep neural network is the best choice for the purpose), and shared pleasant moments with our colleagues at Washington. We hope to be able to attend next year’s edition, which will focus on the extraction of drug-drug interactions (DDI), another interesting task aimed at detecting situations where the use of a combination of drugs may lead to an adverse effect.

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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MeaningCloud’s Artificial Intelligence at EyeforPharma

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Text-based Artificial Intelligence for the Pharma Industry

At MeaningCloud, we are proud to sponsor the Eye for Pharma Conference. Data, Evidence and Access Summit 2017. November 13-14th, 2017 – Philadelphia, US. MeaningCloud’s value proposition for the conference can be summarized as Text-Based Information with Artificial Intelligence.

Eye for Pharma is about demonstrating and communicating value, no matter which department you’re in. Whether it’s exploring innovative uses of real-world evidence (RWE) or creating new outcomes-based pricing models, only by embracing the power of data can you fully unlock the value of your drugs. It is a great opportunity for learning and networking.

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