Wondering what we these numbers are? These stats depict the active users, of the above mentioned social media platforms, globally. This basically means that almost everyone is active on some or the other social media platform at a given point of time. Social Media is giving voice to the general mass. People discuss issues, debate, opine and review.

The last couple of years have seen a large number of organizations and developers rush towards getting familiar with Machine Learning fundamentals and coming to grips with what it takes to integrate it into their applications. While you can definitely build out your own Machine Learning platform, it is not for everyone and companies like Google are now releasing fully managed API platforms where they expose the Machine Learning platform that they have built over the years. The main value to potential users is that these companies have likely trained their Machine Learning models for years and now the best of these services can be had with a single API call. The latest offering from Google is the Cloud Natural Language API which gives developers insights into unstructured text. A REST API is available to invoke the above functionality and we are going to deep dive into the Sentiment Analysis part of the API to first understand how it works and then build out a Slack Team helper that decodes the sentiment of the text provided to it.

Video analytics platform Valossa just launched Val.ai, a platform to help video creators, advertisers and other video boffins figure out what's going on in video. In addition to computer-vision tricks ("Man on a beach", "car interior", "kitten is surprised"), the platform can do sentiment analyses (person is happy / person is sad / person is confused) and even heart rate analysis based on a high-definition video stream alone. "There are many uses for our technology," explains Ville Hulkko, the company's chief commercial officer. Imagine you are looking for a particular piece of footage of a dog and a ball on the beach, for example. If you don't remember when it was taken, you'll spend a long time looking for the correct video clip.

Wondering what we these numbers are? These stats depict the active users, of the above mentioned social media platforms, globally. This basically means that almost everyone is active on some or the other social media platform at a given point of time. Social Media is giving voice to the general mass. People discuss issues, debate, opine and review.

Inspired by this article about sentiment analysis and this guide to webscraping, I have decided to get my hands dirty by scraping and analyzing a sample of reviews on the website Goodreads. The goal of this project is to demonstrate a complete example, going from data collection to machine learning analysis, and to illustrate a few of the dead ends and mistakes I encountered on my journey. We'll be looking at the reviews for five popular romance books. I have voluntarily chosen books in the same genre in order to make comments text more homogeneous a priori; these five books are popular enough that I can easily pull a few thousands reviews for each, yielding a significant corpus with minimum effort. If you don't like romance books, feel free to replicate the analysis with your genre of choice!

In New Mexico, state Rep. Alonzo Baldonado, a Republican whose district includes Los Lunas, said Wednesday he had not heard of any developments out of Utah. He reiterated his support for the project, saying construction of a data center in New Mexico could have a beneficial ripple effect for the economy.

Information Extraction is the first step in the NLP process.

Sifter provides search and retrieve access to every undeleted Tweet in the history of Twitter. Users can submit historical Twitter estimate requests using a variety of query rules. When the query is done, Sifter generates an email estimating the approximate number of tweets responsive to the query and the cost to get the Data. Once the data is licensed by Texifter, we store the Twitter data in a 30-day free trial Enterprise DiscoverText account where you can perform advanced text analytics to search, filter, cluster, code, and machine classify the data. For more videos, head over to see a collection of recent Texifter videos.

Immersive uses text analytics via machine learning to uncover new insights from unstructured data and has developed a'sentiment index' which allows companies to discern how happy or unhappy customers are based on the content of their emails. Immersive worked with the Victorian Department of Justice to secure text information and allow 10,000 workers in Victoria to find information quickly across multiple systems using text search and indexing technologies. Traditionally an IT department would start with the business requirements, find the data and build an application. "For the last 30 years it was all about converged systems, centralised systems, bringing data in and normalising data… That centralisation or converging of systems was enabling some cost savings and some good analytics," Gnau said.

Organisations are beginning to'listen' to unstructured data found in texts which was previously deemed boring or irrelevant to provide insights, says Evan Harridge, founder of Immersive. Immersive uses text analytics via machine learning to uncover new insights from unstructured data and has developed a'sentiment index' which allows companies to discern how happy or unhappy customers are based on the content of their emails. "In a lot of cases we are looking at opportunities that don't exist, because we can now store and analyse all of this information which was just seen as useless or not valuable," Harridge told Which-50 during The Hadoop Summit in Melbourne last week. "Customers are starting to realise all the conversations and all the email communications we have potentially generate value. Rather than just the summarised information or the headings or the information inside the cost table, we take everything and use it as context."