Social Media Monitoring and Analytics Platform.
Google Image Result for. Some questions on Text Analysis : MachineLearning. Linguamatics' I2E Text Mining Platform Chosen by FDA - businesswire.com. 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. AlchemyAPI - Transforming Text Into Knowledge. Psychographic text analysis classifier for values (i.e. worldviews) - Mattias Östmar.
Keywords Alone Are Not Enough: Why Real Text Analytics Matters. LP Insights Real-Time Business Intelligence. DiscoverText - A Text Analytic Toolkit for eDiscovery and Research. Text Analysis. Google Image Result for. 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.
Attensity Pipeline: Social Media Conversations Analyzed, In Real-Time And In The Cloud At Scale. 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.
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. Google Image Result for. 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. Dave’s Adventures in Business Intelligence » Go, Universe, Go! 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. My session this year covers the next step, which answers the question: Now that my universe is working, how can I make it go faster? Predictive Analytics Use Outside Legal Industry. 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). It seems in all these scenarios, just as in analytics’ use in the legal review space, the motivator is using technology to cut costs.
See the Fobrbes.com article for further reading here. HStreaming and Friends Lexalytics Text Analytics Solution to deliver a novel of Data. 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.
What is noisy text? - Definition from WhatIs.com. API Directory - ProgrammableWeb. UC-enabled "Multimodal Customer Experiences" 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. 1. 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. Lexalytics.com. Classification is a few value proposition for text analytics - it allows users to quickly drill into articles of interest and look at trends over time.
Setting up a classification scheme can be a lot of work. The common techniques are: Using queries to bucket documents Using a machine learning model based on tagged document sets. Roistr - Semantic Analysis and Text Analytics. Predictive Analytics: NeuralNet, Bayesian, SVM, KNN. 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. The tuning parameters in Neural network includes the number of hidden layers, number of neurons in each layer, as well as the learning rate.
There are no fixed rules to set these parameters and depends a lot in the problem domain. The learning happens via an iterative feedback mechanism where the error of training data output is used to adjusted the corresponding weights of input. An interesting philosophical kerfuffle. Interesting comments by David Berry at Stunlaw on Object-Oriented Ontology (OOO) based around what is the philosophical purpose of OOO and what are the political questions that arise from a clarification of that philosophical purpose. David writes a great (semi-) polemic on OOO, by fundamentally questioning the importance of knowing what it is like to be a thing, and why this form of knowing should be privileged within a philosophical system.
The flat ontology of OOO is framed (correctly in my view, which is why I am opposed to OOO although sympathetic to OOO as a way to approach things and how they “thing”) as a de-privileging of humans as a co-constructor of meaning, knowledge and understanding. As the pivot which my PhD thesis precariously rests, it would be foolish of me to disagree with David’s objection to this position; unsurprisingly I do not disagree. Flat ontology. What do your online conversations really say? Des Viranna is the Sydney-based general manager for customer intelligence at SAS Australia and New Zealand. SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. The world has reached the point where billions of digital comments are posted daily on public web forums, blogs and social media sites such as Facebook and Twitter. In March 2012, Twitter announced that it had 140 million active users, sending 340 million tweets per day, amounting to an exponential volume of ‘letters’ – 140 characters at a time – sent worldwide.
Despite these millions of words communicated in text conversation daily, it is possible to analyse words and phrases to provide a thorough understanding of the hot topics of discussion, as well as society’s sentiment, from all around the world. Beyond Goals: Site Search Analytics from the Bottom Up. Avinash Kaushik demonstrated that site search analytics (SSA) is a powerful tool you can use to assess customer intent quantitatively. Insightful analytics blog. Text analytics broadens insight possibilities – Digital Reasoning. Using Google's N-Gram Corpus. Turns out I'm not crazy. Text Analytics for Unstructured Information Management - Information Management Newsletters Article. Popcomm-ontology - A population and community ontology. Call: Journal of Robotics 2012 issue on Cognitive and Neural Aspects in Robotics with Applications. Call for Papers: Special Journal Issue on Cognitive and Neural Aspects in Robotics with Applications 2012 (CNAR’12), Journal of Robotics URL:
Improving the Productivity of Knowledge Workers. The term content classification is best understood in an enterprise information context, defined by the following concepts. Brain of mat kelcey. Friday Data Story: Manufacturing Ontologies and Semantic Web. Optimizing Semantic Search. Home > News > Optimizing Semantic Search. Sports Are The Semantic Focus In Britain At The BBC And In Brazil At Globo. Semantic technology is scoring more goals in the sports world. The BBC, for example, which created the FIFA World Cup 2010 website that leveraged semantic technology, is at it again as London prepares for the 2012 Summer Olympics.
Terkait – Chrome Widget for semantic analysis of web content. Powered by VIE. IKS technology has reached Google Chrome’s Webstore. Urgent tasks of modern Text Linguistics. Semantic Arts. Text Analytics. Information Interaction.