Business experiments: Business experiments, experimental design and AB testing are all techniques for testing the validity of something – be that a strategic hypothesis, new product packaging or a marketing approach. It is basically about trying something in one part of the organization and then comparing it with another where the changes were not made (used as a control group). It's useful if you have two or more options to decide between. Visual analytics: Data can be analyzed in different ways and the simplest way is to create a visual or graph and look at it to spot patterns. This is an integrated approach that combines data analysis with data visualization and human interaction.

Last week, completed its acquisition of for $26.2 billion. After winning a purported bidding war with, Microsoft again battled the Customer Relationship Management (CRM) leader in front of EU regulators. Salesforce argued that the acquisition would make Microsoft too powerful in the CRM market, an argument the regulators rejected. The stakes were much higher than that. In fact, the CRM card was a red herring that Microsoft played masterfully.

As part of my Technology and Innovation MBA program at Ted Rogers School of Management, I took a data and knowledge management course which teaches students the principles and practices of knowledge management. The second part of the course delves on tools used in data management and analytics. Although the theoretical part of the course was a bit dry, the hands-on portion was very interesting and exposed students to several different tools to capture, clean and analyze data. One of the tasks given to students was to capture and analyze twitter data. Although students had access to Netlytics, which is a neat cloud-based text and social network analysis tool that also collects Twitter data, students were encouraged to find other ways to collect Twitter data.

MIT researchers have designed a new machine-learning system that can learn by itself to extract text information for statistical analysis when available data is scarce. This new "information extraction" system turns machine learning on its head. It works like humans do. When we run out of data in a study (say, differentiating between fake and real news), we simply search the Internet for more data, and then we piece the new data together to make sense out of it all. That differs from most machine-learning systems, which are fed as many training examples as possible to increase the chances that the system will be able to handle difficult problems by looking for patterns compared to training data.

Most successful information extraction systems operate with access to a large collection of documents. In this work, we explore the task of acquiring and incorporating external evidence to improve extraction accuracy in domains where the amount of training data is scarce. This process entails issuing search queries, extraction from new sources and reconciliation of extracted values, which are repeated until sufficient evidence is collected. We approach the problem using a reinforcement learning framework where our model learns to select optimal actions based on contextual information. We employ a deep Qnetwork, trained to optimize a reward function that reflects extraction accuracy while penalizing extra effort. Our experiments on two databases – of shooting incidents, and food adulteration cases – demonstrate that our system significantly outperforms traditional extractors and a competitive meta-classifier baseline.

This is part of a new series of articles: once or twice a month, we post previous articles that were very popular when first published. These articles are at least 6 month old but no more than 12 month old. The previous digest in this series was posted here a while back. Below is our fourth edition. Top 20 Big Data Experts to Follow (Includes Scoring Algorithm) Text Classification & Sentiment Analysis tutorial / blog Learn Everything about Sentiment Analysis using R 1.5 TB dataset of anonymized user interactions released by Yahoo Fuzzy Matching Algorithms To Help Data Scientists Match Similar Data

Andrew Greenstein, an app developer from San Francisco, started journaling a few months ago. He tries to write for five minutes every day, but it's challenging to set aside the time. Still, he's read that journaling reduces stress and can help with goal-setting, so he's trying to make it a habit. At the Disrupt London Hackathon, Greenstein and his team built The Emotion Journal, a voice journaling app that performs real-time emotional analysis to detect the user's feelings and chart their emotional state over time. By day, Greenstein is the CEO of SF AppWorks, a digital agency.

Text Classification for Sentiment Analysis – Eliminate Low Informat... Why and how to use random forest variable importance measures (and ...

With dozens of powerful text mining features, including access to free and premium Gnip Twitter data, DiscoverText provides software tools to quickly and accurately evaluate text data. Data scientists know that cleaning data can be very time consuming. Users of DiscoverText build custom machine classifiers or "sifters" to find the most relevant items. DiscoverText shortens a process that used to last weeks or months; our machine-learning sifters are created in hours or just a few minutes. We support technical integrations with Twitter and SurveyMonkey.

So you report with reasonable accuracies what the sentiment about a particular brand or product is. After publishing this report, your client comes back to you and says "Hey this is good. Now can you tell me ways in which I can convert the negative sentiments into positive sentiments?" – Sentiment Analysis stops there and we enter the realms of Opinion Mining. Opinion Mining is about having a deeper understanding of the review that was written. Typically, a detailed review will not just have a sentiment attached to it. It will have information and valuable feedback that can literally help to build the next strategy.