Category Archives: Application Areas of Text Analytics

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

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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Automatic Emotion Recognition

In our previous post, we made an introduction to emotion recognition to celebrate the release of the publication of our automatic 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 automatic emotion recognition pack. We will talk about the criteria and considerations we’ve followed in our approach.

Emotion Recognition

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Text Analytics for the Contact Center of the Future

The contact center is a crucial component of the customer experience and increasingly incorporates more channels based on unstructured information. In this post we analyze how advanced semantic analysis can be used to get the most out of the contact center of the future.

The Rise of the New Contact Center

Interest in the contact center has multiplied by its greater role as an essential component of the customer experience. New interaction channels (bots, chats, social) add to the traditional email and telephone and enable innovative ways to connect with clients in both inbound and outbound contact centers, both internal to companies of all types and in those operated by BPO vendors to provide outsourced services.

In this way, the contact center (traditionally known as call center) has ceased to be a cost center to become a tool for proactively communicating with and understanding the market, for multichannel business development and for generating value for the company.
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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 via the Internet — a big part of it as text — and as we know, emotions tend to intensify if left undealt with.

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 expressed by the author of the text 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.