Non-negative matrix factorization (NMF) is a technique for finding latent representations of data. The method has been applied to corpora to construct topic models. However, NMF has likelihood assumptions which are often violated by real document corpora. We present a double parametric bootstrap test for evaluating the fit of an NMF-based topic model based on the duality of the KL divergence and Poisson maximum likelihood estimation. The test correctly identifies whether a topic model based on an NMF approach yields reliable results in simulated and real data.

Algorithmia started out as an online marketplace for -- can you guess it? Many of these algorithms that developers offered on the service focused on machine learning (think face detection, sentiment analysis, etc.). Today, with the boom in ML/AI, that's obviously a big draw and Algorithmia is now taking its next step in this direction with the launch of a new… Read More

When you think of artificial intelligence (AI), the word "emotion" doesn't typically come to mind. But there's an entire field of research using AI to understand emotional responses to news, product experiences, movies, restaurants, and more. It's known as sentiment analysis, or emotion AI, and it involves analyzing views – positive, negative, or neutral – from written text to understand and gauge reactions. Sentiment analysis can be used for survey research, social media analyses, and tracking psychological trends. Picture software that scans articles, reviews, ratings, and social media posts to determine sentiment changes for hotel guests.

Update 10/25/17 3:53 PM: A Google spokesperson responded to Motherboard's request for comment and issued the following statement: "We dedicate a lot of efforts to making sure the NLP API avoids bias, but we don't always get it right. This is an example of one of those times, and we are sorry. We take this seriously and are working on improving our models. We will correct this specific case, and, more broadly, building more inclusive algorithms is crucial to bringing the benefits of machine learning to everyone." John Giannandrea, Google's head of artificial intelligence, told a conference audience earlier this year that his main concern with AI isn't deadly super-intelligent robots, but ones that discriminate.

Google developed its Cloud Natural Language API to give customers a language analyzer that could, the internet giant claimed, "reveal the structure and meaning of your text." Part of this gauges sentiment, deeming some words positive and others negative. When Motherboard took a closer look, they found that Google's analyzer interpreted some words like "homosexual" to be negative. Which is evidence enough that the API, which judges based on the information fed to it, now spits out biased analysis. The tool, which you can sample here, is designed to give companies a preview of how their language will be received.

Movie reviews can be classified as either favorable or not. The evaluation of movie review text is a classification problem often called sentiment analysis. A popular technique for developing sentiment analysis models is to use a bag-of-words model that transforms documents into vectors where each word in the document is assigned a score. In this tutorial, you will discover how you can develop a deep learning predictive model using the bag-of-words representation for movie review sentiment classification. How to Develop a Deep Learning Bag-of-Words Model for Predicting Sentiment in Movie Reviews Photo by jai Mansson, some rights reserved. The Movie Review Data is a collection of movie reviews retrieved from the imdb.com

Text data preparation is different for each problem. Preparation starts with simple steps, like loading data, but quickly gets difficult with cleaning tasks that are very specific to the data you are working with. You need help as to where to begin and what order to work through the steps from raw data to data ready for modeling. In this tutorial, you will discover how to prepare movie review text data for sentiment analysis, step-by-step. How to Prepare Movie Review Data for Sentiment Analysis Photo by Kenneth Lu, some rights reserved.

Q&A is even more straightforward than task-oriented spoken dialogue, as chatbots can provide answers directly to users' questions, such as "How much does an adult panda weigh?" In terms of IoT, Trio's technologies have been applied to a wide range of smart devices, including Xiaomi's Mi TVs, Mi AI speakers, and Smartisan smartphones. They enable Mi TVs and Mi AI speakers to interact with users through voice recognition. Microsoft XiaoIce remains a non-task oriented chatbot, while Baidu's Duer has developed into a platform called DuerOS, similar to Amazon's voice interaction platform, Alexa.

In this talk I will show data engineers and architects how to run real-time TensorFlow Inception Image Recognition on images captured by remote sensors and images in tweets and facebook posts. In the same flow I will also demonstrate how to apply real-time sentiment analysis and intelligent routing of data to Phoenix, Email and Slack. I will elaborate on a number of different sentiment analysis frameworks available for use within Apache NiFi including Python NLTK, Stanford CoreNLP, Python SpaCy and Python TextBlob. This talk will be a deep dive into how to manage complex dataflow pipelines ingesting from multiple streaming sources including social, public open data feeds, logs, drones, RDBMS and IoT with transformations, deep learning, machine learning and business rules. Data engineers will be shown the power of Apache NiFi for loading diverse sources of data, applying transformations in-stream, routing based on attributes, adding sentiment data to workflows, running deep learning algorithms in stream and storing data into Apache Phoenix on HBase and Apache Hive as ORC tables.

First we have to register our account for the twitter API so for that login into your twitter account and open https://apps.twitter.com/app/new So after that you got registered for using Twitter API and you are now on your detail page .Click on the Keys and Access Token ….so these are very important for us to use twitter api . So now its time to install Dependency in our machine .For sentiment analysis we require only two dependency: