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Analyzing Human Data: Take a Dive to Find Out What Your Customers Really Feel - Content... - 0 views

contentmarketinginstitute.com/...man-data-customers-really-feel machine learning blog sentiment analysis customers marketing
shared by cezarovidiu on 30 Oct 14 - No Cached
  • What really interests me, and what I think should interest marketers, is what I’ll call signals – one of which is intent. Intent is critical because it can predict action. For example, “Is this person shopping to buy a product like my product?” “Is this person unhappy and needing some form of attention?” “Is this person about to return the product for a reason that is addressable?”
  • Sentiment is one ingredient of intent. If someone is happy, sad, angry … that can be determined via sentiment analysis technologies.
  • Many tools struggle with context.
  • ...9 more annotations...
  • An example I hear over and over again is “thin” – good when you’re talking about electronics, but bad if you’re talking about hotel walls or the feel of hotel sheets. To do sentiment analysis correctly, you need refinement. You need customization for particular industries and business functions.
  • The market, unfortunately, is polluted with tools that claim to have sentiment abilities, but are too crude to be usable. Even with refinement (e.g., the ability to handle negators and contextual sentiment), approaches that deliver only positive and negative ratings don’t take you very far.
  • There are definitely easy, inexpensive entry points that can meet basic, just-getting-started needs: tools for social listening, survey analysis, customer service (handling contact-center notes, for instance), customer experience (via analysis of online reviews and forums), automated email processing, and other needs. These technologies are user friendly, available on demand, as a service.
  • Text mining:
  • Digital Reasoning, Luminoso and AlchemyAPI.
  • Image recognition and analysis: Image analysis now automatically identifies brand labels in pictures.
  • VisualGraph (now owned by Pinterest), Curalate, Piqora (nee Pinfluencer), and gazeMetrix.
  • Emotional analysis in images, audio, and video: These companies promote analysis of speech and facial expression primarily for structured studies
  • • Affectiva conducts webcam emotional analysis for media and ad research, including development tools to integrate emotional study in mobile apps. • Emotient performs emotional analyses in retail environments, evaluating signage, displays, and customer service. • EmoVu by Eyeris tests the engagement level of both short- and long-form video content. • Beyond Verbal studies emotion based on a person’s voice in real time.
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Yammer Embed | Yammer Developer - 0 views

developer.yammer.com/connect yammer integration apex
shared by cezarovidiu on 29 Oct 14 - No Cached
  • network permalink (see below)
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Why BI projects fail -- and how to succeed instead | InfoWorld - 0 views

www.infoworld.com/...-fail-and-how-to-fix-that.html bi implementare projects
shared by cezarovidiu on 28 Oct 14 - No Cached
  • A successful initiative starts with a good strategy, and a good strategy starts with identifying the business need.
  • The balanced scorecard is one popular methodology for linking strategy, technology, and performance management. Other methodologies, such as applied information economics, combine statistical analysis, portfolio theory, and decision science in order to help firms calculate the economic value of better information. Whether you use a published methodology or develop your own approach in-house, the important point is to make sure your BI activities are keyed to generating real business value, not merely creating pretty, but useless, dashboards and reports.
  • Next, ask: What data do we wish we had and how would that lead to different decisions? The answers to these questions form top-level requirements for any BI project.
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  • Instead a team of data experts, data analysts, and business experts must come together with the right technical expertise. This usually means bringing in outside help, though that help needs to be able to talk to management and talk tech.
  • Nothing makes an IT department more nervous than asking for a feed to a key operational system. Moreover, a lot of BI tools are resource hungry. Your requirements should dictate what, how much, and how often (that is, how “real time” you need it to be) data must be fed into your data warehousing technology.
  • In other words, you need one big feed to serve all instead of hundreds of operational, system-killing little feeds that can’t be controlled easily.
  • You'll probably need more than one tool to suit all of your use cases.
  • You did your homework, identified the use cases, picked a good team, started a data integration project, and chose the right tools.
  • Now comes the hard part: changing your business and your decisions based on the data and the reports. Managers, like other human beings, resist change.
  • oreover, BI projects shouldn't have a fixed beginning and end -- this isn't a sprint to become “data driven.”
  • A process is needed
  • and find new opportunities in the data.
  • Here's the bottom line, in a handy do's-and-don'ts format: Don’t simply run a tool-choice project Do cherry-pick the right team Do integrate the data so that it can be queried performance-wise without bringing down the house Don’t merely pick a tool -- pick the right tools for all your requirements and use cases Do let the data change your decision making and the structure of your organization itself if necessary Do have a process to weed out useless analytics and find new ones
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Installing Hadoop for Fedora & Oracle Linux(Single Node Cluster) | accretion infinity - 0 views

accretioninfinity.wordpress.com/...racle-linuxsingle-node-cluster install hadoop bigdata blog ***
shared by cezarovidiu on 24 Oct 14 - No Cached
  • Hadoop is a framework written in Java for running applications on large clusters of commodity hardware and incorporates features similar to those of the Google File System (GFS) and of the Map Reduce computing paradigm. Hadoop’s HDFS is a highly fault-tolerant distributed file system and, like Hadoop in general, designed to be deployed on low-cost hardware. It provides high throughput access to application data and is suitable for applications that have large data sets.
  • Some of the Hadoop projects we will talk about are: HDFS : A distributed filesystem that runs on large clusters of commodity machines. Map Reduce: A distributed data processing model and execution environment that runs on large clusters of commodity machines. Pig: A data flow language and execution environment for exploring very large datasets. Pig runs on HDFS and MapReduce clusters. HBase: A distributed, column-oriented database. HBase uses HDFS for its underlying storage, and supports both batch-style computations using MapReduce and point queries (random reads). ZooKeeper: A distributed, highly available coordination service. ZooKeeper provides primitives such as distributed locks that can be used for building distributed applications. Oozie: Oozie is a workflow scheduler system to manage Apache Hadoop jobs.
  • Oracle Linux as the operating system and Hadoop 1.1.2 or 1.2.0
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