Category Archives: Application Areas of Text Analytics

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

Text Analytics market 2015: Seth Grimes interviews MeaningCloud’s CEO

Seth GrimesSeth Grimes is one of the leading industry analysts covering the text analytics sector. As part of his annual year-past/look-ahead report on this technology and market developments, Seth polled a group of industry executives, asking their thoughts on the state of the market and prospects for the year ahead.

José Carlos González, CEO of Daedalus / MeaningCloud, was one of the selected executives. In the interview, Seth and José Carlos discuss industry perspectives, technology advances and the “breadth vs depth” dilemma faced by many text analytics vendors.

This is an excerpt from the interview:

Roads to Text Analytics Commercialization: Q&A with José Carlos González, Daedalus

What should we expect from your company and from the industry in 2015?

Voice of the Customer (VoC) analytics — and in general, all the movement around customer experience — will continue being the most important driver for the text analytics market.

The challenge for the years to come will consist in providing high-value, actionable insights to our clients. These insights should be integrated with CRM systems to be treated along with structured information, in order to fully exploit the value of data about clients in the hands of companies. Privacy concerns and the difficulties to link social identities with real persons or companies, will be still a barrier for more exploitable results.

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Interested? Read the rest of the interview –featuring market developments and company and product strategies- on Seth Grimes’ blog.

Voice of the Customer in the banking industry

“The Voice of the Customer (VoC) is a market research technique that produces a detailed set of customer wants and needs, organized into a hierarchical structure, and then prioritized in terms of relative importance and satisfaction with current alternatives.“

Voice of the Customer (VoC)

The Voice of the Customer (VoC) is not a new concept. In one way or another, it’s been included in quality assurance processes for years, and yet, its full integration in the workflow is a pending tasks for many companies. The Voice of the Customer allows you to listen, interpret and react to what’s being said, and then monitor the impact your actions have over time.

The current challenge companies are facing comes from the volume of data available. In this digital age, feedback is ever-growing and not just limited to the periodic surveys sent to clients. Word-of-mouth has gone digital and has become more relevant than ever: everyone with a Twitter or a Facebook account has an opinion, and more often than not, it’s about the products and services they consume.

A typical client

A client

As so many other sectors, banking needs to figure out how to translate this first-hand source of knowledge their clients are providing into something useful, something that can be used in the company’s decision-making process.

Voice of the Customer combines two key aspects of information extraction: the need to know in detail what the customer is talking about and to interpret correctly his feelings about it. The former gives a quantitative view of the feedback obtained while the latter gives a more qualitative analysis, measuring what clients think a company is doing right or wrong.

The banking domain has the added difficulty of providing an extremely wide array of products and services, each one of them with very specific subcategories and received through completely different channels.

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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 (now MeaningCloud) 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

Love Parade Duisburg

Love Parade Duisburg

Anticipate incidents

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.

Flood in Elizondo, Navarre, 2014

Flood in Elizondo, Navarre, 2014

Enrich the available information

Social networks enable the instant sharing of images and videos that are often sources of information of the utmost importance to know the conditions of an emergency scenario before the arrival of the assistance services. User-generated contents can be incorporated to an incident’s record in real time, in order to help clarify its magnitude, the exact location or an unknown perspective of the event.

 

 

Text Analytics technology

Logo MeaningCloud

For the analysis of social content, the text analytics semantic technology (text mining) of MeaningCloud is employed. Its cloud services are used to:

  • Identify the language of the message
  • Classify the message according to a taxonomy (ontology) developed for this scenario (accidents of various kinds, assaults, natural disasters, gatherings, etc.)
  • Extract the mentioned entities (names of people, organizations, places) and the message’s relevant concepts
  • Identify the author or transmitter of each tweet.
  • Extract the geographic location of the transmitter and the incident
  • Extract the time of the message and the incident
  • Classify the impact of the message
  • Extract audiovisual (pictures and videos) and reference (links to web pages, attached documents…) material mentioned in the tweet for documenting the incident
  • Group automatically the messages relating to a same incident within an open record
  • Extract tag clouds related to incidents

Twalert Console

Twalert ConsoleA multidimensional social perspective

Text analytics components are integrated into a web application that constitutes a complete social dashboard offering three perspectives:

  • Geographical perspective, with maps showing the location of the messages’ transmitters, with the possibility of zooming on specific areas.
  • Temporal perspective: a timeline with the evolution of the impact of an incident on social networks, incorporating sentiment analysis.
  • Record perspective: gathering all the information about an incident.

Twitter Accidente Trafico

LT-Accelerate

Telefónica and Daedalus (now MeaningCloud) at LT-Accelerate

Telefónica and Daedalus (now MeaningCloud) will jointly present these solutions at the LT-Accelerate conference (organized by LT-Innovate and Seth Grimes), which will be held in Brussels, on December 4 and 5, 2014. We invite you to join us and visit our stand as sponsor of this event. We will tell you how we use language processing technologies for the benefit of our customers in this and other industries.

 

Register at LT-Accelerate. It is the ideal forum in Europe for the users and customers (current or potential) of text analysis technologies.

Telefonica_logo

 

 

 

 

 

Jose C. Gonzalez (@jc_gonzalez)

[Translation from Spanish by Luca de Filippis]

The Role of Text Mining in the Insurance Industry

What can insurance companies do to exploit all their unstructured information?

A typical big data scenario

Insurance companies collect huge volumes of text on a daily basis and through multiple channels (their agents, customer care centers, emails, social networks, web in general). The information collected includes policies, expert and health reports, claims and complaints, results of surveys, relevant interactions between customers and no-customers in social networks, etc. It is impossible to handle, classify, interpret or extract the essential information from all that material.

The Insurance Industry is among the ones that most can benefit from the application of technologies for the intelligent analysis of free text (known as Text Analytics, Text Mining or Natural Language Processing).

Insurance companies have to cope also with the challenge of combining the results of the analysis of these textual contents with structured data (stored in conventional databases) to improve decision-making. In this sense, industry analysts consider essential the use of multiple technologies based on Artificial Intelligence (intelligent systems), Machine Learning (data mining) and Natural Language Processing (both statistical and symbolic or semantic).

Most promising areas of text analytics in the Insurance Sector

Fraud detection

Detección de Fraude

According to Accenture, in a report released in 2013, it is estimated that in Europe insurance companies lose between 8,000 and 12,000 million euros per year due to fraudulent claims, with an increasing trend. Additionally, the industry estimates that between 5% and 10% of the compensations paid by the companies in the previous year were due to fraudulent reasons, which could not be detected due to the lack of predictive analytic tools.

According to the specialized publication “Health Data Management”, Medicare’s fraud prevention system in the United States, which is based on predictive algorithms that analyze patterns in the providers’ billing, in 2013 saved more than 200 million dollars in rejected payments.

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The Analysis of Customer Experience, Touchstone in the Evolution of the Market of Language Technologies

The LT-Innovate 2014 Conference has just been held in Brussels. LT-Innovate is a forum and association of European companies in the sector of language technologies. To get an idea of the meaning and the importance of this market, suffice it to say that in Europe some 450 companies (mainly innovative SMEs) are part of it, and are responsible for 0.12% of European GDP. Daedalus is one of the fifteen European companies (and the only one from Spain) formally members of LT-Innovate Ltd. since its formation as an association, with headquarters in the United Kingdom, in 2012.

LTI_Manifesto_2014

LT-Innovate Innovation Manifesto 2014

In this 2014 edition, the document “LT-Innovate Innovation Manifesto:” Unleashing the Promise of the Language Technology Industry for a Language-Neutral Digital Single Market” has been published. I had the honor of being part of the round table which opened the conference. The main subject of my speech was the qualitative change experienced in recent times by the role of our technologies in the markets in which we operate. For years we have been incorporating our systems to solve in very limited areas the specific problems of our more or less visionary or innovative customers. This situation has already changed completely: language technologies now play a central role in a growing number of businesses.

Language Technologies in the Media Sector

In a recent post, I referred to this same issue with regard to the media sector. If before we would incorporate a solution to automate the annotation of file contents, now we deploy solutions that affect most aspects of the publishing business: we tag semantically pieces of news to improve the search experience on any channel (web, mobile, tablets), to recommend related content or additional one according to the interest profile of a specific reader, to facilitate findability and indexing by search engines (SEO, Search Engine Optimization), to place advertising related to the news context or the reader’s intention, to help monetize content in new forms, etc.

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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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Use MeaningCloud API with the GATE plug-in

In our attempt to make MeaningCloud API the easiest way to use semantics in your application, today we are proud to present our latest development, a MeaningCloud plug-in for GATE.

GATE (General Architecture for Text Engineering) is an open-source workbench for text engineering that makes use of any kind of language processing component, from document crawling to search, and intelligent semantic annotations in particular.

Benefits for GATE and MeaningCloud API users

The plug-in provides GATE users a new set of multilingual functionalities, from parsing to entity extraction and sentiment analysis. For MeaningCloud users it would mean an easier and quicker method to prototype full applications including crawling, post-processing or indexing on annotated documents.  Besides, if you’re familiar with JAPE rules, it would enable to post-process, mix and match annotations from different processing resources for more complex pipelines. Finally, GATE is ideal for sharing and evaluating pipelines between team members, which increases productivity and produces more accurate results.

Analyzing the Voice of the Customer channels at the Sentiment Analysis Symposium

Sentiment Analysis Symposium 2014

A few days ago we did a presentation at the Sentiment Analysis Symposium of New York. In our talk, we explained how to use text analysis technologies to listen to the different Voice of the Customer channels and get customer insights.

Textalytics at Sentiment Analysis Symposium 2014

For companies is vital to understand the opinions that their actual and potential customers express in new channels that are much more spontaneous and less structured than the traditional surveys (e.g. answers in questionnaires, interactions with contact centers, conversations in social media). The reach, the immediacy and the “emotional“aspect of these channels make them an impressive source of raw materials for obtaining valuable insights.

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Tutorial for feature-level sentiment analysis

Heads up!

This tutorial was made for Textalytics and as such, it has become obsolete. You can read the updated version for MeaningCloud in this post.

MeaningCloud provides an API to carry out advanced opinion mining, Sentiment Analysis, which extracts both a global aggregated polarity of the text and a more in-depth analysis, giving a sentence-level breakdown of the polarity, extracting entities and concepts and the sentiment associated to each one of them.

Cover for Marvel's Black Widow #1

Marvel’s Black Widow #1

What makes MeaningCloud Sentiment Analysis API different is the possibility of defining entities and concepts for each call of the API, allowing you to obtain the same detailed sentiment analysis for entities or concepts specific to the domain of your application.

We are going to use comic book reviews to learn how to use this feature, as it’s a very rich domain in which it’s easy to illustrate how useful user-defined concepts and entities can be. This applies either to this field or to others where sentiment comes into play, such as hotel reviews, Foursquare tips, Facebook status updates or tweets about a specific event.

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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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