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.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

Social media and interplanetary mission -- what do they have in common? Well, they have in common the Mars Orbiter Mission, also known as'Mangalyaan'. It was launched on 5th November by the Indian Space Research Organization (ISRO). It generated a lot of interest across the globe among millions of people on Social Media networks. In this blog, we analyze how Twitterati reacted to this news. Asia was by far the most interested in the subject, covering 74% of all the tweets on'Mangalyaan'.

Analyzing sentiments is a very subjective exercise. He has his own software company which is a mid-sized software one and that which was doing fairly well and at one time, he tried to analyze the broader sentiment about the brand value of his company on the open market. The overall brand sentiment turned out to be negative and even more surprising was the fact that it leaned towards the most negative scale. This surprised him because the other parameters that his HR partners provided him were painting a contrasting picture- the attrition rate was low, the employee engagement survey produced positive results etc. Then he did a deep dive into the feedback content and realized that almost all of the comments were negative and that the people who posted feedback were all disgruntled employees and not many employees who were happy posted any kind of feedback on any social forum. They were too busy with their work and adding more value to the organization.

In my previous blog, I showed you how to integrate Stanford CoreNLP with Talend using a simple example. In this post I'll show you how to modify that code in order to make the most of Talend's strengths as a data integration tool. Below is a Talend job I have built to read some tweets from a database (see this blog article for information on how to retrieve tweets with Talend), run the text through the CoreNLP sentiment analysis code, and then write tweets back to the database with the addition of the sentiment. In this particular example, the text to be analysed are tweets coming from a database. However, the same job will work with any string input.

Transparency Market Research has released a new market report titled Text Analytics Market - Global Industry Analysis, Size, Share, Growth, Trends and Forecast 2016 - 2024. According to this report, the global text analytics market revenue stood at US 2.82 bn in 2015 and is expected to reach US 12.16 bn by 2024, at a CAGR of 17.6% from 2016 to 2024. Text analytics is a method of converting unstructured data into a meaningful form for the analysis of customer feedback, product reviews, sentimental analysis, and entity modeling for supporting fact-based decision making. Several techniques of statistical, linguistic and machine learning are used in text analytics solutions for the retrieval of relevant information from unstructured data. The increasing proliferation of textual data has challenged the ability of organizations across various sectors to summarize and understand them to make better business decisions.