Category Archives: Application Areas of Text Analytics

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

Introduction to emotion recognition in text

Emotions govern our daily lives, they are a big part of the human experience and inevitably they affect our decision making. We tend to repeat actions that make us feel happy but we avoid those that make us angry or sad.

Information spreads quickly through the Internet — a big part of it as text — and as we know, emotions tend to intensify if left unchecked.

Thanks to natural language processing, this subjective information can be extracted from written sources such as reviews, recommendations, publications on social media, transcribed conversations, etc. allowing us to understand the emotions the person who has written them expresses and therefore act accordingly.

Emotion

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Case Study: Text Analytics against Fake News

Everybody has heard about fake news. Fake news is a neologism that can be formally defined as a type of yellow journalism or propaganda that consists of deliberate disinformation or hoaxes spread via traditional print and broadcast news media or online social media. It is also commonly used to refer to fabricated or junk news, with no basis in fact, but presented as being factually accurate.

The reason for putting someone’s efforts in creating fake news is mainly to cause financial, political or reputational damage to people, companies or organizations, using sensationalist, dishonest, or outright fabricated headlines to increase readership and dissemination among readers using viralization. In addition, clickbait stories, a special type of fake news, earn direct advertising revenue from this activity.

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Use case: VoC program for retail

Voice of the Customer (VoC) programs have become an established path for retailers to deliver enhanced customer experiences.

Consumer behavior, nevertheless, is always changing. Retailers are rarely able to anticipate these behavioral changes or adapt quickly enough to preserve or grow their market share.

In 2018, a regional supermarket brand with over 800 hundred stores wanted to understand customer experience at every touchpoint in order to identify potential areas of customer frustration.

The company undertook a strategic Voice of the Customer (VoC) program with the aim of systematically and consistently capturing insights from the customer experience.

The program is still running. It comprises of around 23,000 surveys per month, completed by customers at various branches of the supermarket chain.

In retail, listening to the Voice of the Customer to identify the strengths and weaknesses of business is fundamental. Competition is fierce. Given that the scale of information to be analyzed is immense, the company decided to work with MeaningCloud to process the literal answers to the open-ended questions of the surveys, so they need not worry about the amount or the time needed to process them.
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Introducing the Demo for VoC Retail

Illustration showing a group of shops. Voc Retail

At MeaningCloud, we know how important unstructured data is for  Voice of the Customer Analysis; so we’ve defined a model that will allow you to characterize any feedback, focusing on the retail domain, in detail that you receive from your customers.

Our experience in Voice of the Customer Analysis has shown us that to obtain useful results when consolidating or reorienting a business strategy the detection of peculiarities of a specific domain is vital, as much in a linguistic way as a conceptual way, taking into account the identifying characteristics of the brand to be analyzed. For this reason, we have not only developed an analysis model focused on the retail trade, but we have also adapted analytical tools towards the sale of groceries, personal care and homecare in the retail sector.

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Invoking the MeaningCloud Sentiment Analysis API from Minsait’s Onesait Platform

Minsait’s Onesait Platform is an IoT & Big Data Platform designed to facilitate and accelerate the construction of new systems and digital solutions and thus achieve the transformation and disruption of business. Minsait is a brand of Indra: its business unit addressing the challenges posed by digital transformation to companies and institutions.

Minsait has published a post about the procedure to invoke an external API from the integrated flow engine of the Onesait Platform (formerly known as Sofia2).

MeaningCloud integrated with Minsait Onesait Platform

The post titled HOW TO INVOKE AN EXTERNAL REST API FROM THE SOFIA2 FLOW ENGINE? uses as an example the integration of MeaningCloud Sentiment Analysis API (in Spanish).

The article illustrates one of the strengths of MeaningCloud: how easy it is to integrate its APIs into any system or process.


Case study on the voice of the patient for the Pharma industry

Pharmaceutical companies are extending their Voice of the Patient projects to include social media: comments on web forums, surveys, Twitter, and more.

The goal of the proof of concept ordered by one particular pharmaceutical company in Spain was to: ” Collect and analyze the voice of the patient, both quantitatively and qualitatively, from the channels where it is expressed”, including social networks like web forums, Facebook, Twitter, and other systems.

For the pharma industry, it is essential to listen and understand the feedback that their current and potential customers communicate through various means and touchpoints.

Web forums, for instance, gather millions of posts, and function as a meeting point for patients where support, experiences, and wisdom are shared with peers, family members, and friends.

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People Analytics: MeaningCloud book on Amazon!

People Analytics. Data and Text Analytics for Human Resources

People Analytics. Data and Text Analytics for Human Resources. This MeaningCloud book is available on Amazon.

In People Analytics, and in this book, we use the evidence that the data provides to respond to several questions:

  • Which candidate will be high-performing, effective, loyal, and aligned with the corporate culture?
  • How can we measure the economic impact of a training program?
  • How can I segment the workforce to make their actions more effective?
  • Which people are considering leaving the organization?
  • What net benefit will employees contribute throughout time in a particular position?
  • How does employee commitment affect productivity and economic outcomes?
  • How can I design a study that is statistically and mathematically valid?

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Contact center: 6 ways to leverage text and speech analytics

Contact center. Ilustration

At contact centers, text analytics technology provides an unprecedented opportunity to convert customer interactions into business opportunities. We can improve customer experience, boost sales, reduce customer churn and streamline the efficiency of the processes.

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MeaningCloud Release: Sentiment + Nordic Pack

Not long ago we published the first of our Language Packs: the Nordic pack, which includes several text analytics tasks in Swedish, Danish, Norwegian and Finnish.

Among the text analytics tasks supported, there’s one that was missed by many of you: Sentiment Analysis API. Well, no more!

We are happy to announce that from now on you can also analyze sentiment in the four languages included in the Nordic pack. And what’s more, for those of you that are already subscribed to the pack, it has been automatically included and so you can start using it right away without any change in pricing.

MeaningCloud release

For those of you that are not subscribed to the Nordic pack, remember that you can test all our packs full functionality by requesting a 30 day period trial. It’s super easy!

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