Category Archives: Application Areas of Text Analytics

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

A sentiment analysis entirely tailored to your needs with our new customization tool

The adaptation to the domain is what makes the difference between a good sentiment analysis and an exceptional one. Until now, the possibilities of adapting MeaningCloud’s sentiment analysis to your domain relied on the use of personal dictionaries – to create new entities and concepts that the Sentiment Analysis API employed to carry out its aspect-based analysis – or you had to ask our Professional Services Department to develop a tailor-made sentiment model.

Sentiment Models buttonWith the release of Sentiment Analysis 2.1, we incorporated a new customization tool designed to facilitate the creation of personal sentiment models. This tool fully employs our Natural Language Processing technology to enable you to be autonomous and develop —without programming— powerful sentiment analysis engines tailored to your needs.

Other tools for customizing sentiment analysis available on the market, mostly permit to define “bags of words” with either positive or negative polarity. Our tools go far beyond and enable you to:

  • Define the role of a word as a polarity vector (container, negator, modifier), allowing to use lemmas to easily incorporate all the possible variants of each word
  • Specify particular cases of a word’s polarity, depending on the context in which it appears or its morphosyntactic function in each case
  • Define multiword expressions as priority elements in the evaluation of polarity
  • Manage how these custom polarity models complement or replace the general dictionaries of every language.

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Sentiment Analysis 2.1: Migration guide

We have released a new version of our sentiment analysis API, Sentiment Analysis. In Sentiment Analysis 2.1:

  • We’ve changed how the sentiment model is sent in order to enable the use of custom sentiment models across all the APIs that support sentiment analysis.
  • Support to analyze documents and URLs has been added.
  • A configurable interface language has been added to improve multilingual analyses.

As you would see, this is a minor version upgrade, so the migration process will be fast and painless. In this post, we explain what you need to know to migrate your applications from Sentiment Analysis 2.0 to Sentiment Analysis 2.1.
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Books Are a Service

Semantic Publishing and Voice of the Customer understanding for the media&content industry

The reason for publishing being a key industry to take advantage of text analytics is also the reason why the industry finds it so hard to engage with the technology.

Books are a serviceThe reason? Text. And a lot of it. The publishing world has struggled to understand how data relates to text and understand the value of data. This is changing, too slow for many, as the industry moves from seeing themselves as a ‘product’ based company (e.g. making books, e-books or physical) to a ‘service’ based company. In other words smart publishers are starting to see their service to customers as the creator and curator of information. This content is abled to be mixed and mashed-up in dynamic ways across a number of formats. This service is not bound, saddle-stitch or otherwise, to a specific product. This 180-degree perspective change requires publishers to think more directly about customer experience in the same way more traditional service based industries like hospitality or even retail banking.

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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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Text Analytics for Publishing: there’s metadata and smarter metadata

Everyone agrees metadata is great. It helps simplify the management and packaging of content and data. It creates consistency and provenance of your content and data across an organization. Metadata gives you that 35000 feet perspective that is needed to make strategic decisions. This is especially important for publishers whose stock in trade is human language, which is completely opaque to machines whose world consists of zeros and ones. Your customers aren’t calling or emailing you to know what is in such and such database. No. They are contacting you because they want to know what monographs you have by such and such professor or asking you for all the archival material on ‘cats’, ‘World War 2’ or ‘nanotubes’. As a human, you understand exactly what they are looking for. If your ICT has a smidgeon of metadata, you can dig around that such-and-such database and deliver the content and have a happy customer.

Intelligent content for Semantic Publishing

Metadata TagMetadata makes your content more intelligent. That’s why everyone agrees metadata is great. Great until they have to either enter the metadata or maintain the vocabularies. Some organizations are lucky. They have ensured there is support within the workflow and people with the expertise to do the hard work so when that customer searches on the website, they quickly find what they are looking for and go away happy. But, even those lucky few do not live in isolation. There is no publisher of consequence who doesn’t have do deal with 3rd party content and data. A huge amount of additional effort is spent shoehorning 3rd party content into the metadata models of the organization. Every publisher has a workflow that includes completely throwing away existing metadata and spending additional time and wasteful effort to add metadata that their CMS can handle. Does that sound familiar? Does it feel better to know you aren’t the only one?

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MeaningCloud sentiment analysis powers SocialBro’s Twitter platform

The leading social marketing tool vendor applies MeaningCloud’s advanced sentiment analysis to detect the opinion of Twitter users with the highest quality and without having to develop language processing technology.

UPDATE: as of March 2016 SocialBro has been rebranded as Audiense.

SocialBro analyzes over 15 million tweets per month to extract insights that are essential for its clients’ marketing activities and campaigns. And a key ingredient of these insights is the analysis of Twitter users’ sentiment.SocialBro logo

Due to the characteristics of its business, SocialBro had some very demanding requirements in the field of sentiment analysis: a high throughput, great accuracy and the possibility of carrying out aspect-based analyses. Instead of developing its own sentiment analysis technology, SocialBro decided to turn to a specialized supplier to avoid undertaking developments outside its core business. With this aim, they chose MeaningCloud.

MeaningCloud’s Sentiment Analysis API service stands out for its semantic approaches based on advanced natural language processing. It internally employs a syntactic-semantic tree representation of the text on which it deploys the polarity of the different terms. Then, it combines and spreads these polarities according to the morphological category of each term and the syntactic relations among them.

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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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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.


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Voice of the Customer Analysis and Benefits

 

What is the Voice of the Customer?

Social MediaHave you ever wondered why certain products or services undergo radical changes or even disappear from the market (and sometimes return with another trade name)? Does it depend only on the volume of sales or other factors come into play? To answer these questions, we should introduce the concept of “Voice of the Customer Analysis” and find out what it means. This term refers to all those practices which enable to understand what a (real or potential) customer thinks about a product or service. But it is not limited to a simple reading of comments or opinions written upon request -e.g. an online survey-, the issue is much more complex.

In recent years, the types of channels through which customers and users express their opinions, complaints, suggestions or congratulations (yes, these are also important, then we will see why) have multiplied exponentially. Only a decade ago, the channels that permitted the interaction with the business world were significantly fewer, among them we may recall the telephone or pre-compiled polls often sent by traditional mail. In addition, most of the exchanges between customer and company responded to a specific need of the second; in other words, they were requested.

 

How has it changed?

Today, the picture has radically changed.Voice of the Customer Analysis The communication channels are numerous and also allow to interact in different ways through various media (images, audio, video, etc.). And what matters most to us is that this interaction

  • is constant: 24 hours a day, 365 days a year;
  • most of the times is multilingual;
  • does not always follow predefined patterns (many times, it doesn’t even comply with the most basic spelling rules);
  • is unstructured: it is not stored in a traditional database nor organized according to predefined criteria.

There is no doubt that, from a corporate perspective, this enormous amount of information can be highly beneficial!
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