Category Archives: Application Areas of Text Analytics

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

#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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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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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.

drugs

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.

Health and pharma companies can exploit their unstructured information

There are new kinds of data that are specific to the healthcare and pharmaceutical industries (such as electronic health records) as well as data science tools that allow us to extract valuable knowledge from that data.

 

With MeaningCloud, it is possible to identify the costs of medical treatments, their efficiency (cost, benefits, and risks), references to drugs, side effects, or long-term results. That is why our text analytics solution for the healthcare and pharma domains has so much potential.


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See you at the Sentiment Analysis Symposium 2015 in New York

Next July 15-16, New York will host a new edition of the Sentiment Analysis Symposium. This event is your opportunity to keep up with technologies and solutions that help you discover business value in opinions, emotions, and attitudes in social media, news, and enterprise feedback, to further your business goals.

This year, the program has two tracks: a Presentation Track featuring a mix of business and technical presentations and panels and a Workshop Track with longer-form content. See the agenda here.

Sentiment Analysis Symposium 2015
At MeaningCloud,  over the past year we’ve seen an explosion of interest in sentiment analysis from very diverse industries and the Sentiment Analysis Symposium is the premier event to learn about the latest developments in this and related areas, such as social listening and voice of customer analytics. This is the reason why we are sponsoring the Symposium again in 2015.

We’re thrilled to present and collaborate with other leaders in the industry at this year’s event in New York. Our main presentation will be titled: “From Strangers to Acquaintances: Multidimensional Customer Profiling” and will describe how businesses aim to integrate multichannel interactions (social conversations, web behavior, contact center activity) and other data for profiling and segmenting their users in real time. In this context, a winning approach is to combine dimensions like demographics, lifestyle, brand affinity, or intent to better understand your audience and to generate business opportunities.

For more information and registering, please visit the Symposium’s website. And if you want to save 20% in your registration, contact us at news@meaningcloud.com.

Meet us in New York City, at the Sentiment Analysis Symposium, and follow @SentimentSymp.

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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Discover the WHY behind your Customer Scores – A webinar with Seth Grimes

Seth GrimesOn Wednesday June 10th, MeaningCloud will welcome special guest and text analytics thought leader Seth Grimes for a 1-hour webinar on ensuring you are getting the most from your customer feedback.

Seth will explain the importance of text analytics in new Customer Experience / Voice of the Customer scenarios, enabling you to understand massive amounts of unsolicited, unstructured feedback coming form surveys, customer center interactions and social media… in real time.

And the MeaningCloud Team will show how you can efficiently put these ideas into practice using our easy-to-use, customizable and affordable Meaning-as-a-Service tools.

Whether you are in the market research or customer experience management business or you are an end customer willing to take your customer insights to the next level, this webinar is for you.

We hope you will join us for this special event.

Improve Customer Experience Management with Text Analytics

Discover the WHY behind your Customer Scores – A webinar with Seth Grimes

Wednesday, June 10th, 2015; 8am PST/11am EST

UPDATE: this webinar has already passed. See the documentation and recording here.

Distill customer insights from interactions with clients

For companies, it is vital to understand the feedback that their customers -current and potential- express through all types of channels and contact points. That is why brands are extending their
Voice of the Customer (VoC) initiatives to a new territory of unsolicited and unstructured content: comments on surveys, call center verbatims, Twitter… Only automatic processing enables to perform this analysis with the the necessary characteristics of quality, volume, response time and homogeneity.

Try our free demo of VoC

 

#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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