Language Technology vs. Disinformation

Reuters Institute Digital News Report 2020

Following the 2016 US presidential election, many have expressed concern about the effects of false stories (“fake news”), circulated largely through social media. Research from Oxford University’s Reuters Institute for the Study of Journalism has found a long and steady decline in trust in traditional media. (See Reuters Institute Digital News Report 2020). This declining trust coincides with the uprise of social media as a main source of information. In 2020, social media was a source of news for 48 percent of the public, up from 27 percent in 2013, according to the Reuters Institute.

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IAB Taxonomy Level 3 now available in our Deep Categorization API

IAB - Interactive Advertising BureauDigital marketing is becoming a fundamental pillar, by leaps and bounds, in the business plans of practically every business model. Methods are being refined and the search for the connection between brand and user is expected to become increasingly more precise: a related advertisement is no longer sufficient, now the advertisement must appear at the right time and in the right place. This is where categorization proves to be an exceedingly useful tool.

That is why, at MeaningCloud, we have improved our IAB categorization model in English, that is integrated in our Deep Categorization API:

  • Adding a third level of content taxonomy to the hierarchy of categories (IAB Taxonomy Level 3).
  • Improving the precision of pre-existing categories.
  • Including the unique identifiers defined by IAB itself for each of the categories.

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Use of Text Analytics in Marketing Research

With this post, we start a series dedicated to the use of our Text Analytics technology in the field of R&D in different sectors. This post is dedicated to the use of Text Analytics in Marketing Research.  First of all, we would like to thank the researchers who have selected our services (within a wide range of competitors) as a basis for their research or innovation projects.

Marketing Research

The applications of Text Analytics in marketing are countless. That is why more and more companies are using these tools, starting with giants like Nielsen or TNS. Text analysis driven by natural language processing (NLP) is helping to transform digital marketing strategies. There are several uses for it in the company: evaluating marketing impact, optimizing customer service and SEO, making the most of influencer marketing, and significantly improving social listening. We will elaborate on this last topic in a future post.

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Konplik Health: MeaningCloud splits its business of AI services for healthcare and pharma

As a client and friend to MeaningCloud, it is with great pleasure that I share the news that we have established a dedicated business exclusively focused on providing services for the health and pharmaceutical sectors: Konplik Health. This is an exciting step forward to accelerate our growth.

Today we announce to the public the completion of this spin-off from our Artificial Intelligence (AI) businesses with its 22 years experience into this new, independent company. The spin-off will allow both product and management teams to drive increased responsiveness to their customers’ particular needs and achieve faster growth through focused and fit-for-purpose operating models.

Konplik Health

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Scale and optimize your Market Intelligence processes with text analytics

Competing in the 21st century requires a 360º vision of our markets (customers, competitors, suppliers, technologies, regulation). Market Intelligence is becoming increasingly important; but, its dependence on manual processes, that are not very scalable, renders its application in decision making difficult. Deep text analytics allows for more scalable and actionable Market Intelligence.

Market Intelligence: benefits and limitations

Market Intelligence consists of collecting information about a certain market in a broad sense (i.e. its customers, competitors, partners and supply chain, investors, economic, legal and technological environment…) to extract actionable insights that can be used for strategic decision-making.

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Recorded webinar: Deep Text Analytics for More Scalable and Valuable Market Intelligence

Thank you for your interest in our webinar “Use Deep Text Analytics to Achieve More Scalable and Valuable Market Intelligence” held on June 23rd.

We explained how deep text analytics automatically understand detailed Market Intelligence information and enable applications that enable you to identify business opportunities and capture value from your market much more effectively.

In the session we covered these items:

  • Introduction to Market Intelligence
    • Benefits and limitations
  • Applying deep text analytics
    • Integrating multiple sources
    • Discovering business opportunities
    • Understanding our customers in depth
    • Analyzing the environment
    • Detecting signs of growth

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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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


Leverage Text Analytics to Boost your Market Intelligence (webinar)

Market Intelligence and Competitive Intelligence are essential for the survival and progress of companies. Nevertheless, to perform them with the essential quality- and speed-requirements  involves analyzing an extreme volume and variety of information sources: social comments, news, legislation, scientific articles, patents and various external websites.

Market IntelligenceIn many cases this requires a handcrafted process of manually reviewing content to extract the elements of information that really serve to make strategic decisions. A process that does not scale, is inefficient and expensive.

Deep text analysis allows us to optimize and accelerate the process, not only by detecting mentions of companies or brands and categorizing content thematically, but also by automatically extracting meaningful sematic relationships between topics, discovering new emerging issues and summarizing documents to make the use of that wealth of content faster and more efficient.

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Obtain deep customer insights with MeaningCloud

Companies need to analyze the feedback that customers provide to them through a variety of unstructured channels: surveys, interviews, contact center, social media. However, the text analytics solutions available are limited to a shallow analysis of the feedback. In this post we show you how to use deep analytics to get a complete picture of customer opinions, perceptions, emotions and intentions.

Companies need to become customer-focused in order to understand the needs and opinions of their customers and thus, define the proverbial “segment of 1”. This forces us to implement Voice of Customer (VoC) analysis initiatives that go far beyond the typical periodic satisfaction survey with numerical scores, to look for new sources of insights.

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