Category Archives: Application Areas of Text Analytics

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

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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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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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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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.)

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Communication during the Coronavirus (I): Thematic analysis in Spanish digital news media

While it is obvious that the priority during this pandemic is to cure the sick, to prevent new cases from surfacing and to ensure there are economic and social measures in place to help the people and businesses most afflicted overcome the current situation; without a doubt, in the near future, the analysis of content related to the coronavirus that has been generated by the media and social network users will be the object of research for numerous disciplines such as sociology, philology, linguistics, audio-visual communication, and politics, to name a few.

At MeaningCloud we want to do our bit in this area, by applying our experience and our Text Analytics solutions to analyze the enormous volume of information in natural language, in Spanish and in other languages, in Spain and in other countries, given that, unfortunately, this is a global crisis.

This first article in the series centers on the thematic analysis of content that has been generated in Spanish by digital media platforms in Spain over the last month, how it has evolved during this period of time and the informative positioning of the main media platforms in Spain.

These other articles (only available, at the moment, in Spanish) analyse conversation topics on Twitter in Spain (both from the hashtags and general topics perspective and also applying a specific thematic categorization) and the linguistic analysis of presidential speeches related to this crisis.

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Leverage deep text analytics to transform customer feedback into action (webinar)

Customer FeedbackOne of MeaningCloud’s goals is providing you with the best text analytics technology to help you better understand your customers and in recent times we have been launching products in this area: Voice of the Customer, Emotion Recognition, Intention Analysis.

But maybe you haven’t thought about how to use these products in a synergic way to analyze your customers’ feedback through surveys, contact center interactions and social media, and transform that feedback into action.

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Emotion Recognition in MeaningCloud

In our previous post, we made an introduction to emotion recognition to celebrate the release of the publication of our Emotion Recognition pack. We talked about how emotions play a prominent role in the individual and social life of people and how they have a great impact on their behavior and judgements.

We also saw how thanks to Natural Language Processing we can extract the underlying emotions expressed in a text in a fast and simple way and we saw how useful it can be in multiple scenarios.

In this post, we are going to explain in depth how to get the most out of our emotion recognition pack. We will talk about the criteria and considerations we’ve followed in our approach.

Emotions

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