This post is authored by Ollie Newth, Program Manager at Microsoft. Although text often contains highly valuable data for companies, extracting meaningful data from it can be a challenge. The field of text analytics utilizes natural language processing to extract meaningful structured data from text, and often includes areas such as sentiment analysis, entity recognition and linking, and text clustering. The Microsoft Text Analytics API is one of the Cognitive Services that can help you turn unstructured text into meaningful insights. The API is one part of the Cortana Intelligence Suite, a family of services that helps enterprises build large-scale analytics solutions.

Sarcasm is difficult to detect online, whether as a user or as an algorithm. Sentiment analysis can help, but there are limits to its effectiveness. An article from cloud-based social intelligence agency Infegy examines the problems resulting from the use of sarcasm online and offers solutions for more accurately identifying and dealing with sarcasm.

A computer algorithm that can identify patterns in the social media activity of Islamic State supporters could provide clues about where terrorist attacks are likely to occur. Scientists have found they can spot distinct behavioural patterns in the interactions between groups on social media, and it could even help them predict'lone wolf' attacks'. Social media has been a key tool for organisations like Isis to help them recruit supporters and coordinate their activities. Scientists at the University of Miami have used equations used in physics and chemistry to track the constantly shifting behaviour of supporters of Isis (Isis flag pictured). While law enforcement authorities have attempted to keep track of Isis members using social media, they have tended to focus on monitoring the posts made by individuals.

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.

I'm Julie, Director of Ops, back for a quick tutorial of two awesome new offerings we've built for you. I'm on the non-technical / cat loving side of things so I break it down a bit. I also run the Machine Learning Without a PhD group on LinkedIn where jargon isn't allowed. Feel free to join if you'd like #machinelearning4everyone So today you're getting a 2-for-1 (or as my dad likes to call it a "TooFer"). Today's topic will be the 2016 Tony Awards that aired this past Sunday night. Let's begin by heading to your dashboard.

I have question regarding this paper (http://nlp.stanford.edu/ In the paper there are some results on page 7 in Table 1. There are results for All and Root. For the results All they use the results of all nodes of the tree. For Root they use the results on sentence level.

The personal data you share with Facebook and other social platforms is a treasure trove of information that can, according to one UK startup, prove whether or not you would be a good tenant. Score Assured wants to take the data you share privately and publicly with social media and sell it to individuals, employers, and landlords. Tenant Assured, the first tool in the company's potential suite of data mining-and-selling resources, will connect with your social accounts and give landlords a report based on your data. The company says it uses machine learning software to predict what your data means--from your personality to "financial stress." It also rates the "risk" you would be as a tenant.

In anticipation of his upcoming conference co-presentation, Personalized Medicine and Text Analytics at Text Analytics World Chicago, June 21-22, 2016, we asked Dirk Van Hyfte, Senior Advisor for Biomedical Informatics at InterSystems Corporation, a few questions about his work in text analytics. Q: In your work with text analytics, what behavior or outcome do your models predict? A: To support the shift from reactive to pro-active medicine we look for patients who are at risk to develop Sepsis, Hepatitis C and Delirium. In the area of Behavioral Health we support harm reduction projects. Q: How does text analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?

It might be time to consider changing your Twitter password. According to LeakedSource, a site that keeps a database of leaked login credentials, 32,888,300 Twitter usernames and passwords have been hacked and put up for sale on the dark web. The details were most likely obtained through individual malware attacks, instead of an attack on the social media site itself, according to the website. According to LeakedSource, a site that keeps a database of leaked login credentials, 32,888,300 Twitter usernames and passwords have been hacked and put up for sale on the dark web. The words malware, comes from a combination of the words'malicious' and'software'.

Text analysis is about deriving high-quality structured data from unstructured text. Another name for text analytics is text mining. A good reason for using text analytics might be to extract additional data about customers from unstructured data sources to enrich customer master data, to produce new customer insight or to determine sentiment about products and services. Entity extraction,the parsing and extracting of entities from raw text, is a key part of text analytics. In many cases, entity extraction can be turned into automated entity recognition, in which text is parsed and well-understood entities are automatically selected from the text by the software.