Category Archives: APIs

Posts about Meaningcloud’s APIs.

Automatic IAB tagging enables semantic ad targeting

Our Text Classification API supports IAB’s standard contextual taxonomy, enabling content tagging in compliance with this model in large volumes and with great speed, and easing the participation in the new online advertising ecosystem. The result is the impression of ads in the most appropriate context, with higher performance and brand protection for advertisers.

What is IAB’s contextual classification and what is it good for

The IAB QAG contextual taxonomy was initially developed by the Interactive Advertising Bureau (IAB) as the center of its Quality Assurance Guidelines program, whose aim was to promote the advertised brands’ safety, assuring advertisers that their ads would not appear in a context of inappropriate content. The QAG program provided certification opportunities for all kinds of agents in the digital advertising value chain, from ad networks and exchanges to publishers, supply-side platforms (SSPs), demand-side platforms (DSPs), and agency trading desks (ATDs).

The Quality Assurance Guidelines serve as a self-regulation framework to guarantee advertisers that their brands are safe, enhance the advertisers’ control over the placement and context of their ads, and offers transparency to the marketplace by standardizing the information flowing among agents. All this, by providing a clear, common language that describes the characteristics of the advertising inventory and the transactions across the advertising value chain.

Essentially, the contextual taxonomy serves to tag content and is made of standard Tiers, 1 and 2 – specifying, respectively, the general category of the content and a set of subcategories nested under this main category – and a third Tier (or more) that can be defined by each organization. The following pictures represent those standard tiers.
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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.

Screenshot Sentiment Customization

For example, the expression “the interest rate is very high” expressed by a financial service customer may be positive if it refers to deposits, but negative if it has to do with mortgages. With this tool, it is possible to define these different polarities for each case.

And, the use of this tool is included in your MeaningCloud subscription at no additional cost (even in the Free plan).

This sentiment models tool complements our offer for the development of custom semantic resources and contributes to the goal of MeaningCloud of making the highest-quality text analytics available to all developers.

Would you like to know how to apply the sentiment analysis customization tool in a practical scenario? Register for this webinar on May 4th and you will find out.

UPDATE: This webinar has already taken place. See the recording here.

IMPORTANT: Sentiment Analysis 2.1 introduces changes to the API that make it necessary to migrate your applications to this new version. Migration is very simple, and it is explained here. Remember that Sentiment Analysis 2.0 will no longer be operating as of July 7, 2016: plan your migration with time!

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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Know your audience in detail with the beta of our User Profiling API

Would you like to know the members of your audience to the point of finding out their status and affinities, family and professional circumstances, lifestyle, or the topics they’re interested in? What about being able to divide this audience into meaningful and actionable segments? If you run a company that sells products, would you like to be able to discover and understand your ideal customers and optimize your business activities?

Our text analytics technology now enables you to use the content users publish in social media (their declared profiles, their conversations) to profile your audience according to demographic and psychographic attributes.

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New release of MeaningCloud

We have just published a new release of MeaningCloud that affects Topics Extraction, Lemmatization, POS and Parsing, and Text Classification APIs. Although there are several new features in terms of new functionalities and parameters, the most important aspect of this release lies under the hood and essentially consists of a refactoring of the way in which concept-type topics are internally handled, much more in line with the use of other semantic resources. This lays the foundations for better performance and new features related to the extraction of this type of information. Sty tuned for great improvements in this area in future releases.

The other two great lines of this release are the enrichment of the morphosyntactic analysis with information extraction and sentiment analysis elements (which enable new and richer types of analyses that combine the text’s structure with topics and polarity) and a new predefined classification model.

Here are some details about the developments in the different APIs:

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Lemmatization, PoS, Parsing 2.0: Migration guide

We have released a new version of our core linguistic analyzer: Lemmatization, PoS and Parsing. In Lemmatization, PoS and Parsing 2.0:

  • More analysis possibilities have been included to allow you to combine a complete morphosyntactic analysis with other types of analysis such as Sentiment Analysis and Topics Extraction.
  • Configuration options have been changed to provide more flexibility in the analyses and to make the options available more understandable.
  • We’ve refactored our code to:
    • Improve the quality of the concepts/keywords extraction.
    • Make easier and more flexible the use and traceability of user dictionaries.
    • Give the possibility of obtaining a more complex integrated analysis to give flexibility in complex scenarios where the standard output is not enough.
  • A new type of topic has been added, quantity expressions, to cover a specific type of information that was hard to obtain with previous versions.
  • Some fields in the output have been modified, either to give them more appropriate names or to make them easier to use and understand.
  • Some use modes have been retired as the information provided was redundant with what a morphosyntactic analysis already gives.

All these improvements mean the migration process is not as fast as it would be with a minor version. These are the things you need to know to migrate your applications from Lemmatization, PoS and Parsing 1.2 to Lemmatization, PoS and Parsing 2.0.
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Topics Extraction 2.0: Migration guide

We have released a new version of our information extraction API, Topics Extraction. In Topics Extraction 2.0:

  • The topics extracted have been reordered to extract information in a more coherent way.
  • Configuration options have been changed to provide flexibility in the analyses and to make the options available more understandable.
  • We’ve refactored our code with two short-term goals in mind:
    • Improving the quality concepts/keywords extraction.
    • Making easier and more flexible the use of user dictionaries.
  • A new element has been added, quantity expressions to cover a specific type of information that was hard to obtain with previous versions.
  • Some fields at the output have been modified, either to give them more appropriate names or to make them easier to use and understand.
  • A configurable interface language has been added to improve multilingual analyses.

All these improvements mean the migration process is not as fast as it would be with a minor version. In this post, we explain what you need to know to migrate your applications from Topics Extraction 1.2 to Topics Extraction 2.0.
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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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#ILovePolitics: Popularity analysis in the news

If you love politics, regardless of your party or political orientation, you may know that election periods are exciting moments and having good information is a must to increase the fun. This is why you follow the news, watch or listen to political analysis programs on TV or radio, read surveys or compare different points of view from one or the other side.

American politics in a nutshell

American politics

Starting with this, we are publishing a series of tutorials where we will show how to use MeaningCloud for extracting interesting political insights to build your own political intel reports. MeaningCloud provides useful capabilities for extracting meaning from multilingual content in a simple and efficient way. Combining API calls with open source libraries in your favorite programming language is so easy and powerful at the same time that will awaken for sure the Political Data Scientist hidden inside of you. Be warned!

Our research objective is to analyze mentions to people, places, or entities in general in the Politics section of different news media. We will try to carry out an analysis that can answer the following questions:

  • Which are the most popular names?
  • Does their popularity depend on the political orientation of the newspaper?
  • Is it correlated somehow to the popularity surveys or voting intentions polls?
  • Do these trends change over time?

Before we begin

This is a technical tutorial in which we will develop some coding. However, we will try to guide you through the whole process, so everyone can follow the explanations and understand the purpose of the tutorial.

For the sake of generality and better understanding, we will focus on U.S. Politics in English, but obviously you can easily adapt the same analysis for your own country or (MeaningCloud supported) language.

And last but not least, this tutorial will use PHP as programming language for the code examples. However, any non-rookie programmer should be able to translate the scripts into any language of their choice.

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