Full IAB taxonomy for content classification now available

The surge of content marketing, based on the creation and distribution of valuable content, is a reality. The objective is no longer solely to advertise but to engage and offer valuable experiences to the market as well. In this context, precision in content classification is becoming essential. And that’s why we implemented a new automatic content classifier according to the full IAB taxonomy.

Therefore, since including tier 3 of the content taxonomy to the categorization categories of our IAB model last year, we have chosen to complement it by providing the complete ontology. In line with the Content Taxonomy, published in 2017 and updated in 2020 by the IAB Tech Lab (Interactive Advertising Bureau), this classification includes the remaining 60 categories that make up tier 4.  A total categorization of over 698 categories, hierarchized into four tiers, is thus, offered. We have kept the unique identifier that IAB assigns to each one of its categories, as well as their name, through which its parent categories can be found.

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A Quick Introduction to Brand Monitoring and Brand Protection

[EDITOR’S NOTE: This is a guest post by David Bitton, Product Manager at Webhose.io]

Organizations should never underestimate the power of their brand. What starts out as a name, logo, vision, mission statement, website, and perhaps a few employees start to form an organization’s identity. As an organization grows, all of these key parts evolve over time to help customers identify the brand.

But a brand is more than its identity. A brand should evoke emotion from its customers – ideally a positive one – creating brand loyalty and repeat purchases of its goods or products. Loyal customers also refer the brand to friends, family, and acquaintances.

In today’s digital age, building and maintaining a strong brand is so important that brand monitoring plays a crucial role in organizations’ marketing strategy. However, with the rise of the dark web, brand monitoring has evolved to now include Digital Risk Protection. DRP protects the brand’s digital assets from various malicious actors intent on causing the brand and its reputation significant damage.

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MeaningCloud brings multilingual text analytics to 57 languages!

It’s time to celebrate! We are rolling an update to our APIs that allows analyzing texts in fifty-seven languages. Our dream of multilingual text analytics based on our deep semantic approach has come true.

Until now, to analyze texts in different languages, we needed to maintain a model per language. This gives good results, although it was hard and expensive. We can do better.

Zero to near sixty in no time

We have integrated our APIs with deep neural network technology to translate all these languages into English. Thanks to this, our users can analyze texts in many languages maintaining only one model. And no action is required. All the new languages will appear in your test console and are available in the APIs.

The complete list of languages includes Chinese, Hindi, Arabic, Russian, Japanese, Turkish, German, and many others that our customers have requested frequently. This adds to our current offering for English, Spanish, French, Italian, Portuguese, and the languages we were covering partially (in some APIs).

Multilingual Text Analytics

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Machine Learning for NLP/Text Analytics, beyond Machine Learning

In the field of text analytics, aside from the development of categorization models, the application of machine learning (and more specifically, deep learning) has proved to be very helpful for supporting our teams in the process of building/improving rule-based models.

This post analyzes some of the applications of machine/deep learning for NLP tasks, beyond machine/deep learning itself, that are used to approach different scenarios in projects for our customers.

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Accuracy measures in Sentiment Analysis: the Precision of MeaningCloud’s Technology

Accuracy Measures of Commercial Sentiment Analysis APIs

Our clients frequently ask, “what’s the precision of MeaningCloud technology?” How does it compare with other commercial competitors and with state-of-the-art technology? And they demand precise numbers.

That’s not an easy question to answer. Even when there are milliards of research studies on this issue. For the sake of simplicity, let’s concentrate on the well-studied scenario of accuracy measures in Sentiment Analysis. Continue reading


New Excel 365 add-in for Text Analytics!

Our new Excel 365 add-in has finally arrived!

Excel is the preferred tool for many MeaningCloud users. They access MeaningCloud APIs directly from Excel with our add-in. In the last months, we have received a lot of inquiries about Mac support. So, we partnered with Microsoft to build a new multiplatform version.

 

Installation

Installing it is a breeze on all platforms. The new add-in is available in Microsoft AppSource:

https://appsource.microsoft.com/en-us/product/office/WA200002421

Click on Get it Now and follow the instructions.

 

Configuration

You only need your API key to use MeaningCloud. Paste it in the License Key field and you’re ready to start analyzing.

Don’t have one? Create an account for free – no payment method required.

Configuring the MeaningCloud add-in

 

Usage

You can use the APIs directly from the ribbon:

MeaningCloud add-in ribbon

The user interface page describes the different buttons. Paste your texts in the spreadsheet, select the tool in the ribbon, review the parameters and click in Analyze:
Sentiment analysis with MeaningCloud
Take a look at the documentation for more information about add-in usage.

 

But I don’t use Office 365!

No worries. If you use another Excel version, we still offer the previous add-in version. If you don’t use Microsoft Excel at all, you can use our Google Spreadsheets add-on.

 

Questions?

If you have any questions or issues, we will be glad to hear from you. Drop us a line at support@meaningcloud.com and tell us about your experience.

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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