Category Archives: MeaningCloud

This category groups the different aspects of MeaningCloud we talk about in the blog.

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.


sentimentanalysis

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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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Daedalus is now Sngular Meaning

[See the UPDATE section at the end of this post to know about the relationship between Sngular and MeaningCloud, as of June 1st, 2017.]

I am thrilled to announce that Daedalus, the company that I founded in 1998 in Spain, is now part of the Sngular group. This operation is part of a merger of five complementary IT companies to form a corporation based on talent and innovation, with the purpose of serving better our customers in times of accelerated changes.

As a consequence of this M&A operation, Daedalus has been renamed Sngular Meaning, raising series C funding from Sngular, its parent company, to accelerate its international development.

What does this deal mean for MeaningCloud?

Logo MeaningCloudMeaningCloud LLC is the branch of Sngular Meaning in the United States, in charge of development and marketing of our text analytics services. It is our strategic bid to consolidate as an international reference in the field of semantic technologies.

For MeaningCloud, this deal assures:

  • The financial resources for a faster expansion of our international business.
  • New marketing channels through cooperation with our Sngular sibling companies.
  • New opportunities to build specific solutions for vertical markets.

Some figures about MeaningCloud (that quickly become obsolete):

  • 5,000 registered users.
  • 1,000 active users in the last month.
  • 5 million API calls per day.

What is the structure of Sngular?

The companies that form the Sngular group are:

In total, we add up to a total of 300 people, with branches in the United States, Mexico and Spain. We define ourselves as a talent tech team. We will be visible under the domain sngular.team. Our CEO is Jose Luis Vallejo.

Regarding Sngular Meaning, besides the incorporation of Jose Luis Vallejo to the Board of Directors, there are no other changes at the management level. On my side, I will continue as President at Sngular Meaning and CEO at MeaningCloud. We can assure the continuity of our strategy around our trademarks MeaningCloud and Stilus.

When is the kick off?

On October 8th we will make a public presentation of the new Sngular group. This will be an event for employees and customers (by invitation only), but we plan other open events for later.

This is an exciting moment for us. We look at the future with confidence. I am sure that, as members of the Sngular family, we will continue enjoying the affection and support of all of you: customers, business partners and friends. Wish us luck and thank you for remaining at our side!

UPDATE as of June 1st, 2017

Almost two years later, anybody can see that the Sngular merger was a great success. What was founded as an umbrella corporation formed by five sister companies is now a strong IT company with multinational presence, where four of the original founding companies are fully merged. Besides this, other companies have been merged into Sngular through different mechanisms during the last months.

Regarding Sngular Meaning, we have jointly decided to integrate the service-oriented Data Science and Big Data activities in Sngular, while retaining all the activities and assets in the area of Text Analytics and Natural Language Processing in general. The Spain-based company Sngular Meaning (formerly Daedalus) has been renamed MeaningCloud Europe SL, owning 100% of the US-based company MeaningCloud LLC. Sngular maintains a non-controlling interest in our renewed and renamed company.

Long live Sngular! Long live MeaningCloud!

Jose C. Gonzalez

New insights in your contents with the new release of MeaningCloud

We have just published a new release of MeaningCloud with some new features that will change your way of doing text analytics. As a complement to the most common analytical techniques -which extract information or classify a text according to predefined dictionaries and categories- we have included unsupervised learning techniques that enable to explore a series of documents to discover and extract unexpected insights (subjects, relationships) from them.

In this new release of MeaningCloud we have published a Text Clustering API that allows to discover the implicit structure and the meaningful subjects embedded in the contents of your documents, social conversations, etc. This API takes a set of texts and distributes them in groups (clusters) according to the similarity between the contents of each document. The aim is to include in each cluster documents that are very similar to each other and, at the same time, highly different from the ones included in other clusters.

Clustering is a technology traditionally used in the analysis of structured data. What is so special about our API is that its pipelines are optimized for analyzing unstructured text.

Text Clustering API

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Feature-level sentiment analysis

Back when we were called Textalytics, we published a tutorial that showed how to carry out feature-level sentiment analysis for a specific domain: comic book reviews.

Cover for Marvel's Black Widow #1

Marvel’s Black Widow #1

Since then, besides changing our name, we have improved our Sentiment Analysis API and how to customize the different analyses through our customization engine. In this post we are going to show you how to do a feature-level sentiment analysis using MeaningCloud.

One of the main changes in the latest release of our API is the possibility of using custom dictionaries in the detailed sentiment analysis provided by the Sentiment Analysis API. We are going to use comic book reviews to illustrate how to work, but the same process applies to any other fields where sentiment comes into play, such as hotel reviews, Foursquare tips, Facebook status updates or tweets about a specific event.

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Could Antidepressants Be the Cause of Birth Defects?

We agree that it is not typical at all for an Information Technology company to talk about antidepressants and pregnancy in its own blog. But here at MeaningCloud we have realized that health issues have a great impact on social networks, and the companies from that industry, including pharmas, should try to understand the conversation which arises around them. How? Through text analysis technology, as discussed below.

Looking at the data collected by our prototype for monitoring health issues in social media, we were surprised by the sudden increase in mentions of the term ‘pregnancy’ on July 10. In order to understand the reason of this fact, we analyzed the tweets related to pregnancy and childbearing. It turned out that the same day a piece of news on a study issued by the British Medical Journal about the harmful effects that antidepressants can have on the fetus had been published.
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