Tag Archives: big data

Posts related to Big Data

For insurance companies, it is vital to listen and understand the feedback that their current and potential customers express through all kinds of channels and touch points. All this valuable information is known as the Voice of the Customer. By the way, we had already dedicated a blog post to Text mining in the Insurance industry.

(This post is a based upon the presentation given by Meaning Cloud at the First Congress of Big Data in the Spanish Insurance Industry organized by ICEA. We have embedded our PPT below).

More and more insurance companies have come to realize that, as achieving product differentiation at the industry is not easy at all, succeeding takes getting satisfied customers.

Listening, understanding and acting on what customers are telling us about their experience with our company is directly related to improving the user experience and, as a result, the profitability. In the post on Voice of the Customer and NPS, we saw in more detail this correlation between customer experience and benefits.

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Emergency Management through Real-Time Analysis of Social Media

09/October/2014

Language Technology Semantic Processing Smart Cities Social Media Voice of the Citizen

big data emergency management real time analysis social media monitoring use cases

Serving citizens without paying attention to social media?

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

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

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

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

A 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.

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.

Jose C. Gonzalez (@jc_gonzalez)

[Translation from Spanish by Luca de Filippis]

Semantic Analysis and Big Data to understand Social TV

25/November/2013

Content Industry Social Media Social TV

big data natural language processing real time analysis semantic analysis senseidb

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.