Social Media Monitoring and Analytics Platform - Crimson Hexagon Discover Actionable Insights from Big Data Sources If you’ve made it this far into our website, you know that the vast sea of global online conversation is not really a sea at all…it’s a Big Data goldmine waiting to be tapped. Here’s how Crimson Hexagon can ensure that you’re making the most of your brand every day. Crimson Hexagon ForSight™ Platform Empowers You:
call center analytics
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Some questions on Text Analysis : MachineLearning
Linguamatics' I2E Text Mining Platform Chosen by FDA - businesswire.com - Industry News from LT-Innovate Linguamatics, a leader in natural language processing (NLP)-based text mining, has announced that the FDA’s Center for Drug Evaluation and Research (CDER) has licensed its I2E text mining platform to support laboratory research efforts on drug safety. Financial details of the agreement were not disclosed During the term of the license, CDER will use I2E to mine published literature and drug product labels to answer questions relating to a range of biomedical topics, including drug toxicity mechanisms and disease processes. I2E’s NLP-based querying capabilities, coupled with its scalability and flexibility, mean it is ideally suited to answering many challenging, high value questions in life sciences and healthcare by unlocking knowledge buried in the scientific literature and other textual information.
Psychographic text analysis classifier for values (i.e. worldviews) - Mattias Östmar
Keywords Alone Are Not Enough: Why Real Text Analytics Matters
Today’s online businesses have no shortage of customer data: chat transcripts, emails, call recordings, CRM notes, web analytics, survey data, social media including blogs, Facebook, and Twitter… But despite the extraordinary amount of data available, translating this information into holistic, accurate, and actionable insights eludes most organizations. LP Insights combines your chat transcripts with all forms of structured and unstructured data, and delivers actionable insights that empower a wide range of management initiatives across the enterprise. In a quantum leap over existing transcript analysis and reporting processes, LP Insights monitors, measures, and makes sense of customers’ words, behavior, reasons for contact, buying patterns, and sentiments, delivering turn-key intelligence to your desktop, in near-real-time. LP Insights Real-Time Business Intelligence | Products | LivePerson
DiscoverText - A Text Analytic Toolkit for eDiscovery and Research
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Speed, Scale, Automation and Comprehension: The True ROI of Text Analytics — Nectarine Imp
Natural Language Processing HUB – News about NLP
Good business: Scalable document management lends solid legal footing Scalable document management is handy when accommodating changing numbers of documents, users and locations. But it takes on extra significance when a professionally diverse and geographically dispersed group of people use the technology for litigation support and e-discovery as part of legal proceedings. In the legal arena, document management is all about protecting, preserving and controlling access to any information that might be introduced as evidence in a court case.
For many companies, understanding what’s being said about them or their products and services in the real-time social media space will only become more important. Vendors of social and customer analytics solutions are aiming to fill the need: A couple of weeks ago, heavyweight Salesforce said the Twitter firehose will be funneled to its social analytics arm Radian6. Last week, Attensity announced the Attensity Pipeline, which is its foray into providing a semantically annotated social media data stream in real-time, as a cloud service, tapping into the full Twitter firehose as well as public Facebook and Google Plus posts, blogs, forums, and video and review sites. “We have had previous generations of this [technology] used in back end products that were more batch-oriented,” says Catherine van Zuylen, vp, product at Attensity. Attensity Pipeline: Social Media Conversations Analyzed, In Real-Time And In The Cloud At Scale
Converseon Gives Big Data a Human Touch and Added Value Social data provides important insights into the desires, habits and inclinations of customers. While, many platforms survey customer remarks on social media outlets like Facebook and Twitter, such technology is often limited in its “word-spotting” approach which, taken out of context, may yield results irrelevant to a client’s research aims. Converseon’s new text analytics service ConveyAPI combines the speed, processing capacity and scalability of data software with the accuracy, understanding and analyses of social conversations that only a human touch can provide. ConveyAPI addresses the challenge Dr. Phillip Resnik, a Converseon data scientist, explains: “Where scientific jargon, marketing puffery, and a laundry list of features can often obscure what really matters: using a technology meant to measure human expression, are we obtaining the value of a human analysis?”
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What can text analytics learn from Crowdsourcing — Nectarine Imp Or: What we can learn from crowdsourcing Text Analytics can learn a lot from a competing system for getting answers to analytical-like questions. Crowdsourcing is a voluntary effort where collaboration to solve a problem is key. Text analytics is a sort of involuntary collaboration of multiple voices gathered together in a corpus.
I have been selected to speak at the 2012 SAP BusinessObjects User Conference (twitter tag #SBOUC). My topic this year will be a follow-up of sorts to a topic I did last year called, “Designer Essentials.” In the essentials topic I went over the basic requirements of universe design (setting up a connection, adding tables, building joins, and so on). I also covered additional steps that have to be done in order to ensure the correct results, like making sure all SQL traps are resolved. Dave’s Adventures in Business Intelligence » Go, Universe, Go!
Predictive Analytics Use Outside Legal Industry | Discovery Brain In the legal industry, the use of analytics has been on the rise. It’s most often used to speed up document review. Using text analytics, a small, representative data set can be used to quickly identify similar documents in a much larger population, allowing for more targeted, faster document review. Outside of the legal industry though, the technology is also gaining momentum and is seen as a great use for big data and cloud technologies. Forbes.com reports on analytics’ use to predict future events or data patterns such as in fraud detection, production management, or by marketing (for analyzing customer retention).
0 20px 10px Hadoop Summit / Santa Clara, CA (PRWEB) June 13, 2012 Lexalytics, a provider of text analytics and feel and HStreaming, in real-time data analysis platform that is powered by Hadoop, today announced a partnership to deliver a highly scalable, low latency Text Analytics Data Solutions General. This solution enables companies to quickly analyze large text data and their connection with structured data and unstructured others to view the full analysis. solution unparalleled in the market. Combined solution allows customers to analyze thousands of new documents at the same time and to disclose what is in them in real time. HStreaming and Friends Lexalytics Text Analytics Solution to deliver a novel of Data | leblogvideodeluciano.com - Video Streaming
What is noisy text? - Definition from WhatIs.com Noisy text is an electronically-stored communication that cannot be categorized properly by a text mining software program. In an electronic document, noisy text is characterized by a discrepancy between the letters and symbols in the HTML code and the author's intended meaning. Noisy text does not comply with rules the program uses to identify and categorize words, phrases and clauses in a particular language. Idiomatic expressions, abbreviations, acronyms and business-specific lingo can all cause noisy text. It is particularly prevalent in the unstructured text found in blog posts, chat conversations, discussion threads and SMS text messages. Other potential causes include poor spelling and punctuation, typographical errors and poor translations from optical (OCR) and speech recognition programs.
API Directory - ProgrammableWeb
Copyright (C) Unified-View, All Rights Reserved.June 7, 2012 A recent analysis of contact center applications moving into the "cloud" showed that IVR (self-service) applications was the application that was most frequently shifted off premise to a "cloud" based service. This highlights how customers will be accommodated with more flexible self-service options as they start using multimodal smart phones and tablets, rather than traditional voice-only telephones. As I suggested in a previous post, mobile customers will now require an integration of various customer service experiences. It will be a combination of how self-service interfaces are designed for smartphones and tablets, as well as how live assistance is accessed and supported when needed. It also includes proactive, outbound “notifications” and alerts to mobile customers from automated business process applications, and what response options can be given to the mobile recipient. UC-enabled "Multimodal Customer Experiences"
Text Classification for Automated Email Response | VirtualBlackSwanMarketing
I think, therefore I am an Artificial Intelligence If All Human Souls posess Conciousness, then (as I have unassailably logically proved symbolically) according to the many experimental results of the Classic Young's Double-Slit Experiment in technical jargon: I. 'Consciousness' is sometimes an unobserved Wave, when a particle is detected (which-way information) can sometimes maintian it's quantum state regardless. II. 'Consciousness' is whether measured (which-way info.) or no can be detected or not correspondingly. III.
The world is your oyster... And if your world is data, Salience is your pearl. One of the things that makes this engine so great is its ability to categorize content automatically. It can tag high volumes of unstructured text, making it super easy to filter and sort through these documents. Blogs | Lexalytics.com
Roistr - Semantic Analysis and Text Analytics
Continuing from my previous blog in walking down the list of Machine Learning techniques. In this post, we'll be covering Neural Network, Support Vector Machine, Naive Bayes and Nearest Neighbor. Again, we'll be using the same iris data set that we prepared in the last blog. Neural Network Neural Network emulates how the human brain works by having a network of neurons that are interconnected and sending stimulating signal to each other. In the Neural Network model, each neuron is equivalent to a logistic regression unit. Predictive Analytics: NeuralNet, Bayesian, SVM, KNN
An interesting philosophical kerfuffle | Mycotoxin Damage
What do your online conversations really say?
Beyond Goals: Site Search Analytics from the Bottom Up
insightful analytics blog :: Content Analytics
Text analytics broadens insight possibilities – Digital Reasoning
Using Google's N-Gram Corpus
Turns out I'm not crazy
popcomm-ontology - A population and community ontology
Call: Journal of Robotics 2012 issue on Cognitive and Neural Aspects in Robotics with Applications
Improving the Productivity of Knowledge Workers
brain of mat kelcey
Friday Data Story: Manufacturing Ontologies and Semantic Web
Optimizing Semantic Search : TaxoDiary
Sports Are The Semantic Focus In Britain At The BBC And In Brazil At Globo
terkait – Chrome Widget for semantic analysis of web content. Powered by VIE | IKS Blog – The Semantic CMS Community
Urgent tasks of modern Text Linguistics | Long Live Wauconda