Author Archives: Antonio Matarranz

About Antonio Matarranz

Chief Marketing Officer at MeaningCloud

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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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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Hej, hallå, hallo, hei! MeaningCloud supports the Nordic languages

Have you ever needed to detect entities in Danish texts or to classify documents in Swedish?

At MeaningCloud we aim to provide the most advanced text analytics product with the broadest language coverage in the market. That’s why we have just launched a Language Pack that adds to our traditional languages -English, Spanish, French, Italian, Portuguese and Catalan- coverage for the Nordic languages. Continue reading


Recorded webinar: Vertical Packs, VoC, VoE

Thank you all for your interest in our webinar “MeaningCloud Vertical Packs: the Fastest Way to Benefit from Text Analytics” that we delivered last December 20th, where we explained how to customize text analytics with only one click  and we presented our  Packs for the analysis of the Voice of the Customer and the Voice of the Employee.

During the session we covered these items:

  • Introduction to text analytics and MeaningCloud.
  • Why Vertical Packs? How they create value.
  • What are the components of Vertical Packs: models, APIs, integrations.
  • Available Packs: Voice of the Customer, Voice of the Employee.
  • Case study: analysis of the Voice of the Customer.
  • Coming developments: product roadmap

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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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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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, Booking.com, 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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Recorded webinar: Why You Need Deep Semantic Analytics

Last July 13th we delivered our webinar “Why You Need Deep Semantic Analytics”, where we explained how to achieve a deep, automatic understanding of complex documents. Thank you all for your interest.

During the session we covered these items:

  • Automatic understanding of unstructured documents.
  • What is Deep Semantic Analytics? Comparison with conventional text analytics.
  • Where it can be applied.
  • Case study: due diligence process.
  • Ideal features of a Deep Semantic Analytics solution.
  • MeaningCloud Roadmap in Deep Semantic Analytics.

IMPORTANT: you can find a more literary explanation of some of the items we covered, including the due diligence practical case, in this article.

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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Deep Semantic Analytics: A Case Study

Scenarios that can benefit from unstructured content analysis are becoming more and more frequent: from industry or company news to processing contracts or medical records. However, as we know, this content does not lend itself to automatic analysis.

Text analytics has come to meet this need, providing powerful tools that allow us to discover topics, mentions, polarity, etc. in free-form text. This ability has made it possible to achieve an initial level of automatic understanding and analysis of unstructured documents, which has empowered a generation of context-sensitive semantic applications in areas such as Voice of the Customer analysis or knowledge management.

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Why you need Deep Semantic Analytics (webinar)

Achieve a deep, automated understanding of complex documents

Conventional Text Analytics enable a first level of automatic understanding of unstructured content, achieved through its ability to extract mentions of entities and concepts, assign general categories or identify the polarity of opinions and facts that appear in the text. However, these isolated information elements do not reflect the wealth of information provided by these documents and impose limitations when it comes to finding, relating or analyzing them automatically.

Deep Semantic Analytics represents a step beyond conventional text analytics by providing features such as snippet-level granular categorization, detection of complex patterns, and extraction of semantic relationships between information elements in the document.

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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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