Category Archives: Language Technology

Posts about language technology.

What is the Voice of the Employee (VoE)?

Voice of the Employee. Silhouettes with bubbles representing dialog

Finding committed employees is one of public and private organizations’ top priorities. Thus, listening to the Voice of the Employee by systematically collecting, managing and acting on the employee feedback on a variety of valuable topics is essential.

The relationship between Voice of the Employee (VoE) and Engagement is very similar to the one between Voice of the Customer (VoC) and Customer Experience. VoC provides information to improve customer experience. Voice of the Employee promotes employees’ engagement in the company and their work. See: Voice of the Employee, Voice of Customer and NPS

Voice of the Employee collects the needs, wishes, hopes, and preferences of the employees of a given company. VoE considers specific needs, such as salaries, career, health, and retirement, as well as implicit requirements to satisfy the employee and gain the respect of colleagues and managers.
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Text Analytics & MeaningCloud 101

One of the questions we get most often at our helpdesk is how to apply the text analytics functionalities that MeaningCloud provides to specific scenarios.

Users know they want to incorporate text analytics into their processes but are not sure how to translate their business requirements into something they can integrate into their pipeline.

If you add the fact that each provider has a different name for the products they offer to carry out specific text analytics tasks, it becomes difficult not just to get started, but even to know exactly what you need for your scenario.

homer-simpson-confused

In this post, we are going to explain what our different products are used for, the NLP (Natural Language Processing) tasks they are tied to, the added value they provide, and the requirements they fulfill.

[This post was last updated in October 2018 to include our new functionalities.]
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Language Technology Industry meets in Brussels, May 16-17, 2016

Language Technology Industry Summit

Logo_LTi_2016

LT-Innovate, the Language Technology Industry Association, organizes a new edition of its annual Summit. It is the yearly point of convergence between the Language Technology Industry, its clients, research partners and policy makers. According to its Memorandum of Association (London, 2012), LT-Innovate is a non-governmental organization consisting of all parties involved in the field of Language Technologies (LT) and services. Main goals are the promotion of common interests in the successful development, production, delivery and use of language technologies and services and the implementation of services that may help to promote the industry.

LTI Cloud

Besides traditional sections, as Solution Showcases, Technology Spotlights, and Project Results, Language Technology Industry Summit 2016 will serve as the official launch of one of the most important endeavours undertaken by the Association since its inception: the LTI Cloud.

LTI Cloud is the one-stop-shop platform for making available, discovering, assembling, testing and prototyping language technology components. If you are a potential provider of LT APIs (researcher, developer, startup…), and you want to get exposure, testing, or simply customers, consider using LTI Cloud, as it is a ready-to-use platform.

Still in a pilot phase until May 17th, you can be among the first adopters of LTI Cloud. And remember that it serves not only LT providers but also final users. Jochen Hummel, the leader of this initiative, will make the presentation at the Conference. By now, you can take a look at this preview.

Coming back to the Summit, I would like to stress a traditional track: “Customers challenge the Industry”. This year’s challenge comes from Elsevier: “Dynamic Knowledge Stores and Machine Translation”. It will be presented by Michelle Gregory and Pascal Coupet.

MeaningCloud User Profiling API

Being MeaningCloud one of the founding companies of LT-Innovate, we are proud to take an active role again in this year’s event. On Tuesday 17th of May, I will be presenting our recent work on “Automatic Extraction of Rich Customer Profiles from their Activity in Social Networks”. It is about our brand new MeaningCloud API for automatic profiling of Twitter users. User Profiling API allows extracting some important demographics according to different aspects for a given Twitter user: which topics the user talks about, personal and professional information, hobbies and interests, etc. This information extraction is based on a mixed rule-based and machine learning approaches.

Conference Discount Code

Come and join us at the LT-Innovate Summit. And, before registration, do not forget to ask for a special discount code through our helpdesk (support@meaningcloud.com).

Voice of the Customer in the insurance industry

For insurance companies, it is vital to listen and understand the feedback that their current and potential customers express through all kinds of channels and touch points. All this valuable information is known as the Voice of the Customer.  By the way, we had already dedicated a blog post to Text mining in the Insurance industry.

(This post is a based upon the presentation given by Meaning Cloud at the First Congress of Big Data in the Spanish Insurance Industry organized by ICEA. We have embedded our PPT below).  

More and more insurance companies have come to realize that, as achieving product differentiation at the industry is not easy at all, succeeding takes getting satisfied customers.

Listening, understanding and acting on what customers are telling us about their experience with our company is directly related to improving the user experience and, as a result, the profitability. In the post on Voice of the Customer and NPS, we saw in more detail this correlation between customer experience and benefits.

 

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Net Promoter Score (NPS) via Voice of the Customer (VoC)

More and more companies have come to understand that to grow profitably in competitive scenarios, satisfied customers are the key to success. And they know that employees have a fundamental role in achieving a better customer experience.

In this challenge to improve customer loyalty, companies must be able to listen to their customers and understand what they are saying. It is what we call the Voice of the Customer (VoC).

However, a mission — such as customer satisfaction — that lacks a precise measure of success (or failure) is just hot air. Quoting Lord Kelvin, “If you can not measure it, you can not improve it.”

The Net Promoter Score (NPS) has become, for a number of companies, the key metric for measuring customer satisfaction. By the same standard, the mission to get motivated and happy people in an organization also has its key metric: the eNPS (Employee NPS).

As discussed below, in order to improve customer and employee experience, both the NPS and the eNPS need to find the reason that justifies the score given.

When asked What is the primary reason for your score? the NPS and the eNPS collect and analyze the open answers of thousands of customers and employees. Here is where the linguistic technology of Meaning Cloud intervenes.
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Some conclusions from our Text Analytics survey

What does “text analytics” mean to you and your organization? How do you plan to use Text Analytics in 2016? For MeaningCloud, as a text analytics tool vendor, having some answers to these questions is key to understand our market and define our product strategy: this was the purpose of the survey we kept open during some weeks, since the beginning of last October.

Even though the number of respondents was quite low (60) it is definitely possible to draw some conclusions and trends that we summarize in this post.

Applications: customer is first

What is your text analytics application scenario? No doubt this is the main question when one needs to analyze the uses of this technology. In our results, Understanding customer attitudes, behaviors, and needs was the most mentioned scenario (62%), followed by Research (48%) and Content Classification, recommendation, and personalization (43%) as it can be seen in the figure. The following two categories were Customer service, improving customer experience (40%) and Brand/reputation management (38%), which means that everything related to customer understanding, improving customer experience, and managing the brand lead the text analytics application area, coping 3 of the 5 first positions.

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#ILovePolitics: Political discourse analysis in social media

We continue with the #ILovePolitics series of tutorials! We will show how to use MeaningCloud for extracting interesting insights to build your own Political Intel Reports and, at the same price, turning you into a Data Scientist giant in the field of Social Media Analytics.

political issues

Political issues

Politics and Social Media Analytics

Our research objective is to study and compare the discourse of different politicians during the electoral campaign, using their messages in Twitter. We are going to compare tweets by the four most popular (mentioned) politicians in our previous tutorial: Barack Obama (@barackobama), Hillary Clinton (@HillaryClinton), Donald Trump (@realDonaldTrump) and Jeb Bush (@JebBush).

  • What are their key messages?
  • What do they focus on?
  • Are really there different ways of doing politics?

Before we start, three remarks: 1) we will focus on U.S. Politics, in English language, but the same analysis can be adapted for your own country or language as long as it is supported in MeaningCloud, 2) this is a technical tutorial: we will develop some coding, but in general, everyone can understand the purpose of this tutorial, and 3) although this tutorial will use PHP, any non-rookie programmer can translate the programs to any language.

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How might your organization employ Text Analytics in 2016?

Help us design the best Text Analytics tool

If you are a MeaningCloud user or are otherwise involved in Content Analytics or Text Mining, we’d like to hear your opinion.

We want to know what “text analytics” means to you and your organization. We are researching current trends and issues in the market, both business- and solution-related, including adoption by industry and business function, successes and failures, and requirements for the software tools of the future.

Please take part in our survey. Respondents will receive a copy of the conclusions.

The survey is at https://www.surveymonkey.com/r/SurveyTextAnalytics

and it’s open till the end of  November 18th.

Take the Survey

Thank you!

An Introduction to Sentiment Analysis (Opinion Mining)

In the last decade, sentiment analysis (SA), also known as opinion mining, has attracted an increasing interest. It is a hard challenge for language technologies, and achieving good results is much more difficult than some people think. The task of automatically classifying a text written in a natural language into a positive or negative feeling, opinion or subjectivity (Pang and Lee, 2008), is sometimes so complicated that even different human annotators disagree on the classification to be assigned to a given text. Personal interpretation by an individual is different from others, and this is also affected by cultural factors and each person’s experience. And the shorter the text, and the worse written, the more difficult the task becomes, as in the case of messages on social networks like Twitter or Facebook.


sentimentanalysis

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What You Need To Know about Text Analytics

You have enough to worry about. You know your industry inside and out. You know your products and services and how they compare with the competition’s strengths and weaknesses. In business, you have to be an expert in a range of topics. What you don’t need to worry about are the ins and outs of every technology, algorithm and software program.

This is especially true of an inherently complex technology such as natural language processing. As a business owner you have enough to worry about. Do you really have time to understand morphological segmentation? Text analytics should be just another tool in your toolbox to achieve your business goals. The only thing you need to know is what problems you have that can be solved by natural language processing. Anaphoric referencing? Don’t worry about it. We have it covered it, along with anything else you might need from language technology.

Text Analytics

What do you do need to know about text analytics?

Text analytics goes by many names: natural language processing (NLP), text analysis, text mining, computational linguistics. There are shades of difference in these terms, but let the expert work that out. What you need to know is that these terms describe a variety of algorithms and technology that is able to process raw text written in a human language (natural language) to provide enriched text. That enrichment could mean a number of things:

  • Categorization – Classifying text according to themes, categories or a taxonomy
  • Topic Extraction – Identifying key named entities and concepts in the text such as people, places, organizations, and brands
  • Sentiment Analysis – Detecting whether the text is talking about those concepts in a positive or negative light

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