Category Archives: APIs

Posts about Meaningcloud’s APIs.

#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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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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Improve your Customer Experience Management with Text Analytics (recorded webinar)

Last June 10th we presented our webinar “Discover the WHY behind your Customer Scores – Improve your Customer Experience Management with Text Analytics”, featuring industry expert Seth Grimes.

The goal of the webinar was to ensure you are getting the most from your Customer Experience / Voice of the Customer initiatives, using text analytics to understand massive amounts of unsolicited, unstructured customer feedback in real time.

User Profiling and Segmentation

The agenda, with contributions from Seth and members of the MeaningCloud team, was:

  • Text analytics in Customer Experience (CX) management. Why is it important?
  • How text analytics complements/amplifies “traditional” CX? What specific benefits does it bring: understanding the reason behind the scores, extending to new, untapped feedback sources, analyzing CX in big data contexts … What new applications does it enable?
  • What text analytics techniques are applicable: text classification, information extraction, sentiment analysis, user profiling…
  • Analysis of some real scenarios/projects: survey analysis, contact center interaction, market research, social media analysis.
  • How to implement this easily with MeaningCloud: APIs, personalization tools, add-in for Excel.

For those of you interested, below you can find the webinar’s slides and recording.

And, if you want to give MeaningCloud a try and see how it can take your customer feedback analysis to the next level, register and use it for free here.

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Sentiment Analysis 2.0: Migration guide

We have released a new version of one of our more popular APIs: Sentiment Analysis. In Sentiment Analysis 2.0:

  • The rules used for defining polarity terms have been greatly improved, adding new operators and making the models used much more flexible, which in turn leads to better results.
  • Sentiment analysis is now done at more levels, allowing to identify more complex syntactic structures and to obtain more detailed information about how the polarity is expressed.
  • More configuration options have been added related to the morphosyntactic analysis over which the sentiment analysis is carried out.
  • The architecture of the service has changed, leading to a tenfold improvement in the response time.
  • An integration with the Lemmatization, PoS and Parsing API has been added in order to ease the way of creating applications that use the information provided by both APIs.
  • Dictionary customization has been fully integrated in order to get out the most out of its functionality.

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 Sentiment Analysis 1.2 to Sentiment Analysis 2.0.
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#TuitometroMadrid: a demonstration of MeaningCloud’s capabilities

Using MeaningCloud’s APIs we have developed in a few days a social monitoring tool for a highly topical theme: the local and regional elections in Spain.

Due to the great expectations raised by the upcoming elections of May 24th, several initiatives have appeared that try to analyze the conversation in social media about the different policy options.
We would like to show you one of them, which won’t be given the medal for arriving first, but will definitely win one for being the fastest (we will explain later this apparent contradiction).
At MeaningCloud we have developed #TuitometroMadrid (in Spanish), an application that enables to analyze thoroughly and in real time the conversation on Twitter about the political parties and candidates shortlisted for the Community of Madrid and Madrid’s City Council.

TuitometroMadrid Home

#TuitometroMadrid allows to monitor the buzz, the opinions, and the relevant terms and hashtags around each political option and to compare them aggregately.

TuitometroMadrid Sentiment

Why do we say that it is the fastest tool? Because, besides the fact that it provides the information virtually in real time (and not as post hoc reports), it’s development has been the quickest: by using MeaningCloud’s APIs, an engineer implemented all the semantic analysis of social content in less than one day.
Apart from its usefulness as an informative tool, #TuitometroMadrid is a demonstration that semantic analysis technologies serve to solve real problems in a simple and affordable way.

Would you like to embed semantic analysis into your applications in the easiest, most customizable and affordable way? Use MeaningCloud for free.

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Monitor corporate reputation with our new API

Do you need to understand the impact that social media and news have on your corporate reputation (or your customers’)? Now, a new MeaningCloud API enables you to analyze all that information in real time and structure it according to the dimensions of the most common reputational schemes.

Some customers have been demanding us a precise way of understanding the impact that opinions from social media and other channels have on a company.

Corporate Reputation APISocial monitoring tools provide a basic sentiment analysis that in the best of cases identifies that a certain comment (e.g. a tweet) expresses a positive or negative opinion about an entity, and use the aggregated data in diagrams and temporal evolutions. Nevertheless, analyzing a so multifaceted reality as the reputation of a company requires a more granular opinion analysis.

Although it is usually identified with online reputation, corporate reputation is a different concept: it consists of an aggregate of the opinions and perceptions that the public has about a certain company. And it’s a multidimensional characteristic, since those opinions are assessed around a series of more or less standard axes:  Financial situation, Product quality, Innovation, Strategy…

A more social, real-time reputation analysis

So far, corporate reputation has been measured by interviewing and conducting surveys to customers and analysts, and it has been provided in the form of static periodic reports. But the number of customers who are not satisfied with a “snapshot” a posteriori -based on a few opinions- is constantly increasing. Now they wish to add to the picture the impact that social and traditional media have on their reputation, and access that information in a more up-to-date and actionable way to detect potential reputational crises well in advance.

With this MeaningCloud update we have added a new Corporate Reputation API, which enables to include in the reputational analysis the huge amount of spontaneous opinions expressed in all kinds of media (social networks, forums, blogs, news websites) in real time.

New corporate reputation API

This API performs a reputational tagging of text: it receives a document (tweet, piece of news, comment in a survey), detects the mentioned companies, identifies the reputational dimensions involved and extracts the polarity that affects each one of them. All this with a high level of accuracy and granularity, distinguishing opinions about different companies than can coexist in a single sentence. The result is presented in the form of standard tags that can be used to aggregate, relate, detect trends, generate alerts, etc.

Under the hood, the corporate reputation API uses a highly optimized pipeline that incorporates information extraction, automatic classification and polarity analysis techniques. In addition, it is based on standard interfaces and features SDKs that enable to integrate it into any available monitoring application or tool. More information here.

Using this API you can complement the traditional reputational studies with a more agile and immediate analysis of the impact of social media, news, etc., and thus manage more dynamically such an important asset of your company.

And don’t forget that with this MeaningCloud update we include two APIs from our previous product, Textalytics: Linguistic Analysis and Text Proofreading.

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