Category Archives: Application Areas of Text Analytics

Posts about Application Areas of NLP / Natural Language Processing / Text Analytics

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.

Continue reading


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.

Continue reading


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


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.

Continue reading


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.

Continue reading


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.

Continue reading


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.)

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.

Continue reading


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.

Continue reading


Recorded webinar: Deep text analytics to transform customer feedback into action

Last April 29th we delivered our webinar “Leverage deep text analytics to transform customer feedback into action”. Thank you all for your interest.

In it we explained how to use Meaning Cloud’s products in a synergic way to analyze your customer feedback through surveys, contact center interactions and social media, and level up your customer insights.

During the session we covered these items:

  • Leveraging unstructured customer feedback: benefits and challenges
  • Text analytics to the rescue… but with limitations
  • How to use deep text analytics to extract more actionable insights
    • Pre-made Insights
    • Adaptation
    • Development
  • understand the opinions, perceptions, emotions and intentions of your customers.

Interested? Here you have the presentation and the recording of the webinar.

(También presentamos este webinar en español. Tenéis la grabación aquí.)

Continue reading