Category Archives: Social Media

Posts about Social Media

#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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Why We Believe in the Voice of the Customer


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” and analyze 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 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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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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Exploring Social Media for Healthcare Data

People enjoy sharing information through social media, including healthcare data. Yeah, it is true! And it constitutes the starting point of the research work titled ‘Exploring Spanish health social media for detecting drug effects’, which aims at following social media conversations to identify how people talk about their relation with drug consumption. This allows identifying possible adverse effects previously unknown related to these drugs. Although there is a protocol to communicate to the authorities the identification of a drug adverse effect, only a 5 – 20% of them are reported. Besides, conversations around drugs, symptoms, conditions and diseases can be analyzed to learn more about them. For example, it is possible to see how people search for specific drugs using social media, while others sell them, perhaps illegally. Many others talk about mixing alcohol with drugs or other illegal substances. Of course, one cannot believe everything that appears on the Internet this is another issue—, but it can highlight some hypothesis for further research.


Some researchers from the Advanced Databases Group at Carlos III University of Madrid have carried out the mentioned study, designing hybrid models to capture the needed knowledge to identify adverse effects. The Natural Language Processing platform which supports the implementation of the analysis process based on such models is MeaningCloud. The customization capabilities provided by the platform have been decisive to include specific vocabulary and medical domain knowledge. As we know, the names of drugs and symptoms might be complex and, in some cases, difficult to write properly. The algorithm’s results are promising, with a 10% increase in recall when compared to other known algorithms. You can find further details in the scientific paper published by the BMC Medical Informatics and Decision Making Journal.

These developments have been part of the TrendMiner project, and are now available in the prototype website TrendMiner Health Analytics Dashboard, which shows people’s comments about antidepressants gathered from social media. The console displays the mentions of antidepressants and related symptoms and, by clicking on any of them, their evolution over time. Moreover, the source texts analyzed to compute those mentions are shown at the bottom, with labels highlighting the names of drugs, symptoms or diseases, and any relations among them. Such relations might say if a drug is indicated for a symptom or if a disease is an adverse effect of the mentioned drug. The prototype also allows searching by the ATC code (Anatomical Therapeutic Chemical Classification System) and the corresponding level according to this classification scheme. So, if you mark the ‘By Active Substance’ selector, you are searching any drug containing the active substance of the product you inserted in the search box. Furthermore, the predictive search functionality makes easier to find the right expression for a drug or disease. Please, have a look at the prototype and tell us what you think about it. If you find a chart useful, you can even tweet it from there! Any comment is more than welcome.

#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.

Emergency Management through Real-Time Analysis of Social Media

Serving citizens without paying attention to social media?

App Llamada Emergencias

The traditional access channels to the public emergency services (typically the phone number 112 in Europe) should be extended to the real-time analysis of social media (web 2.0 channels). This observation is the starting point of one of the lines which the Telefónica Group (a reference global provider of integrated systems for emergency management) has been working in, with a view to its integration in its SENECA platform.

Social dashboard for emergency management

At Daedalus we have been working for Telefónica in the development of a social dashboard that analyzes and organizes the information shared in social networks (Twitter, initially) before, during and after an incident of interest to emergency care services. From the functional point of view, this entails:

  • Collecting the interactions (tweets) related to incidents in a given geographical area
  • Classifying them according to the type of incident (gatherings, accidents, natural disasters…)
  • Identifying the phase in the life cycle of the incident (alert or pre-incident, incident or post-incident)

Benefits for organizations that manage emergencies

Anticipate incidents

Love Parade Duisburg

Love Parade Duisburg

Anticipation of events which, due to their unpredictability or unknown magnitude, should be object of further attention by the emergency services. Within this scenario are the events involving gatherings of people which are called, spread or simply commented through social networks (attendance to leisure or sport events, demonstrations, etc.). Predicting the dimensions and scope of these events is fundamental for planning the operations of different authorities. We recall in this respect the case of the disorders resulting from a birthday party called on Facebook in the Dutch town of Haren in 2012 or the tragedy of the Love Parade in Duisburg.
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Analyzig audience and opinion on live events for Social TV

By the end of June, we took part in the TVX 2014 international conference on interactive experiences for television and online video with a demo entitled “Numbat – Tracking Buzz and Sentiment for Second Screens”. On it we showed our work and expertise on social media analytics applied to television and live events, combining semantic analysis technologies and real-time data processing to get metrics on social audience and opinions about each feature of the live program or event.

Social TV is not only a continuously growing area, but also a thoroughly mature one, with dozens of companies interested in user interaction and social marketing. Social media are giving particular importance to this interaction between users and TV broadcasts. To realize how far the social conversation about international events goes, you could take a look at Twitter’s recap on FIFA World Cup 2014 group stage.

cristianoDuring the conference we could see the ways industry and researchers are taking to make their point on Social and Interactive TV. For example, second screen applications allow viewers to have a deeper understanding on what they are watching, providing additional information related to the broadcast (usually ad hoc and synchronized for a better user experience) or through automatic trends discovery. Other approaches try to help users finding the right TV programs by studying their habits and behaviors when watching television.

For our demo, we chose to visualize two World Cup matches being played at the same time: United States – Germany and Portugal – Ghana.

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Sentiment Analysis tool for your brand in 10 minutes!

Have you ever tried to understand the buzz around your brand in social networks? Simple metrics about the amount of friends or followers may matter, but what are they are actually saying? How do you extract insights from all those comments? At MeaningCloud, we are planning a series of tutorials to show you how you could use text analytics monitor your brand’s health.

Today, we will talk about the fanciest feature: Sentiment Analysis. We will build a simple tool using Python to measure the sentiment about a brand in Twitter. The key ingredient is MeaningCloud Media Analysis API which will help to detect the sentiment in a tweet. We will also use Twitter Search API to retrieve tweets and the library matplotlib to chart the results.

Brand monitoring

Listening to what customers say on social networks about brands and competitors has become paramount for every kind of enterprise. Whether your purpose is marketing, product research or public relations, the understanding of sentiment, the perception and the topics related to your brand would provide you valuable insights.  This is the purpose of MeaningCloud Media Analysis API, make easier the extraction of these insights from the myriad of comments that are potentially talking about a brand. This tutorial will guide you through the process of building an application that listens to Twitter for your brand keywords and extract the related sentiment.
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Semantic Analysis and Big Data to understand Social TV

We recently participated in the Big Data Spain conference with a talk entitled “Real time semantic search engine for social TV streams”. This talk describes our ongoing experiments on social media analytics and combines our most recent developments on using semantic analysis on social networks and dealing with real-time streams of data.

Social TV, which exploded with the use of social networks while watching TV programs is a growing and exciting phenomenon. Twitter reported that more than a third of their firehose in the primetime is discussing TV (at least in the UK) while Facebook claimed 5 times more comments behind his private wall. Recently Facebook also started to offer hashtags and the Keywords Insight API for selected partners as a mean to offer aggregated statistics on Social TV conversations inside the wall.

As more users have turned into social networks to comment with friends and other viewers, broadcasters have looked into ways to be part of the conversation. They use official hashtags, let actors and anchors to tweet live and even start to offer companion apps with social share functionalities.

While the concept of socializing around TV is not new, the possibility to measure and distill the information around these interactions opens up brand new possibilities for users, broadcasters and brands alike.  Interest of users already fueled Social TV as it fulfills their need to start conversations with friends, other viewers and the aired program. Chatter around TV programs may help to recommend other programs or to serve contextually relevant information about actors, characters or whatever appears in TV.  Moreover, better ways to access and organize public conversations will drive new users into a TV program and engage current ones.

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