Author Archives: Antonio Matarranz

About Antonio Matarranz

Chief Marketing Officer at MeaningCloud

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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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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You can now use MeaningCloud with RapidMiner

Expand text analytics with the tools to create the most sophisticated predictive models

At MeaningCloud, we have just launched a feature that enables users to incorporate our text analytics into complex predictive models based on structured data. With our new Extension for RapidMiner you can directly embed our semantic analysis engines into the process pipelines defined in this popular analytical tool.

RapidMiner is an open-source platform for data science, recognized as a leader in the field of advanced analytics tools. RapidMiner is used for preparing data, creating predictive models, validating them, and embedding them into business processes quickly and easily .

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