Most of the systems on the market will clock anywhere around 55-65% for unseen data, even though they might be 85% accurate in their cross-validations. At this juncture, it's important to realize that sentiment analysis is critical for any system monitoring customer reviews or social media posts. Hardly had the business world caught up with a sentence level sentiment analysis, we are now moving to aspect level sentiment analysis - more directed & granular, adding to the complexity. The question is this - can we do something to augment our sentiment analysis? For the past few months, I have been using context and relationship extraction to augment sentiment analysis.

The Dirichlet process and its extension, the Pitman-Yor process, are stochastic processes that take probability distributions as a parameter. These processes can be stacked up to form a hierarchical nonparametric Bayesian model. In this article, we present efficient methods for the use of these processes in this hierarchical context, and apply them to latent variable models for text analytics. In particular, we propose a general framework for designing these Bayesian models, which are called topic models in the computer science community. We then propose a specific nonparametric Bayesian topic model for modelling text from social media. We focus on tweets (posts on Twitter) in this article due to their ease of access. We find that our nonparametric model performs better than existing parametric models in both goodness of fit and real world applications.

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

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.