Sifting through lots of documents can be difficult and time consuming. Without an abstract or summary, it can take minutes just to figure out what the heck someone is talking about in a paper or report.
And, if you need to get through hundreds of documents – good luck.
In an earlier post, we introduced the Sentiment Analysis algorithm and showed how easy it was to retrieve the sentiment score from text content through an API call.
In this post, we’ll show how to build a sentiment analysis pipeline that grabs all the links from a web page, extracts the text content from each URL, and then returns the sentiment of each page.
Dear Friends of Algorithmia,
Every year we like to take a small step back and reflect on what we achieved in 2016. Last year we went from private beta to a top Seattle startup.
2016, as we hoped it would be, was a big year for us. We provided more than 30,000 developers with access to over 2,700 algorithmic microservices – a humbling achievement for our small, dedicated team.
Sentiment analysis is the process of identifying the underlying opinion or subjectivity of a given text. It generally categorizes these opinions on a scale from negative to positive. Some sentiment analysis algorithms include the neutral sentiment, too.
These sentiments scores are generally used to identify the level of satisfaction of a given product or service. This helps companies and organizations better understand their users, and make impactful changes to their products.
In the last issue of Emergent // Future for 2016, we look at Uber’s self-driving cars in San Francisco, Google’s new autonomous car company, how Mark Zuckerberg built an AI assistant for himself, what Amazon, Google, and Microsoft are up to.
Reading emotional expression is one of the most difficult tasks for humans, let alone computers. Two people looking at the same photo might not agree whether someone is grimacing or grinning. Until recently, computers weren’t much better at the job, either.
Fortunately advances in deep learning has brought us speed, efficiency and accuracy in detecting people’s emotions in photos.
This week we look at AMD’s effort to take on NVIDIA in the high performance computing space, recap what happened at NIPS2016, and highlight the best things to read that explore technology through the lens of artificial intelligence, data science, and the shape of things to come.
This week we look into the efforts to open source artificial intelligence by Google DeepMind and OpenAI, how Amazon AWS is re:Inventing its platform for AI with new services and FPGA’s for EC2.
At Algorithmia, we believe in democratizing access to state of the algorithms. That’s why we’re excited to provide an AWS AMI and pipeline for creating your own style transfer models.