Sentiment essentially relates to feelings; attitudes, emotions and opinions. Sentiment Analysis refers to the practice of applying Natural Language Processing and Text Analysis techniques to identify and extract subjective information from a piece of text. A person's opinion or feelings are for the most part subjective and not facts. Which means to accurately analyze an individual's opinion or mood from a piece of text can be extremely difficult. With Sentiment Analysis from a text analytics point of view, we are essentially looking to get an understanding of the attitude of a writer with respect to a topic in a piece of text and its polarity; whether it's positive, negative or neutral.

Remember that video US president Donald Trump tweeted in which he wrestled someone to the ground and started punching them? It was genuine footage of Trump from a popular wrestling show but he had the image doctored to replace the victim's head with the CNN logo and added the hashtag #FraudNewsCNN, just in case we didn't get the memo that he really dislikes the news network. But are these news networks as biased as he thinks? Do Fox News journalists say mostly nice things while those at CNN are busy portraying him in a negative light? Artificial intelligence (AI) in the form of sentiment analysis and stance detection can tell us what is really happening.

Advances in of Natural Language Processing and Machine Learning are broadening the scope of what technology can do in people's everyday lives, and because of this, there is an unprecedented number of people developing a curiosity in the fields. And with the availability of educational content online, it has never been easier to go from curiosity to proficiency. We gathered some of our favorite resources together so you will have a jumping off point into studying these fields on your own. Some of the resources here are suitable for absolute beginners in either Natural Language Processing or Machine Learning, and others are suitable for those with an understanding of one who wish to learn more about the other. The resources on this post are 12 of the best, not the 12 best, and as such should be taken as suggestions on where to start learning without spending a cent, nothing more!

At least 80% of enterprise data is unstructured, contained in the myriad text-based social conversations that are happening every day. Unlocking the hidden value of text through predictive analytics is imperative to the understanding of customers' opinions and needs, to make better, more informed business decisions. A whopping 90% of this data is actually completely underutilized when it comes to data strategies and data analytics techniques. It's very easy for humans to consume and make sense of unstructured data, but machines don't find it as easy. At the rate it's being created, it's almost impossible for humans to consume this information at the rate that it's growing.

Among the vast business applications of artificial intelligence, Slator has been keeping a close eye on neural machine translation (MT). However, the boundaries between MT and broader tech like natural language processing (NLP) are sometimes fuzzy. The services resulting from these technologies are often adjacent: translation on one side and chatbots on another. In fact, some companies combine them into a single service--multilingual chatbots, for instance. This is why a recent slew of significant funding rounds in the NLP space has caught our attention. In June 2017, Italy-based venture incubator H-Farm acquired language technology services provider CELI, also headquartered in Italy, in a leveraged buyout of 100% of its shares.

Juggernaut is an experimental Neural Network, written in Rust. It is a feed-forward neural network that uses gradient descent to fit the model and train the network. Juggernaut enables us to build web applications that can train and evaluate a neural network model in the context of the web browser. This is done without having any servers or backends and without using Javascript to train the model. Juggernaut's developer-friendly API makes it easy to interact with.

Four members of our research team spent the past week at the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017) in Copenhagen, Denmark. The conference handbook can be found here and the proceedings can be found here. The program consisted of two days of workshops and tutorials and three days of main conference. Videos of the conference talks and presentations can be found here.The conference was superbly organized, had a great venue, and a social event with fireworks. With 225 long papers, 107 papers, and 9 TACL papers accepted, there was a clear uptick of submissions compared to last year.

Programming languages are the primary tool of the software development industry. Since the 1940's hundreds of them have been created and a huge amount of new lines of code in diverse programming languages are written and pushed to active repositories every day. We believe that a source code classifier that can identify the programming language that a piece of code is written in would be a very useful tool for automatic syntax highlighting and label suggestion on platforms, such as StackOverflow and technical wikis. This inspired us to train a model for classifying code snippets based on their language, leveraging recent AI techniques for text classification. We collected hundreds of thousands of source code files from GitHub repositories using the GitHub API.

The landscape of data is ever-changing, meaning analysts need to evolve both their thinking and data collection methods to stay ahead of the curve. In many cases, data that might have been considered unique, uncommon or unattainably expensive just a few years ago is now widely used and often very affordable. It is the analysts who take advantage of these untapped data sources, while they remain untapped, who can reap the rewards by gaining a competitive advantage before the rest of their industry or peers catch on. This type of data is often referred to as alternative data, and with the ever-increasing levels of data available in the modern world comes the opportunity to gain unique insights, competitive industry advantage, and boosted profits. It is perhaps no surprise then to hear that the scramble to get hold of such data has been dubbed the new gold rush.

Europe is a hotbed of AI innovation. Here are 25 AI start-ups to watch out for in 2017 and beyond. There are literally hundreds of promising companies pushing the boundaries of artificial intelligence and machine learning in Europe. We've included a number of Israel-based start-ups because they too fall into the sphere of influence of European investors. And, judging by recent acquisitions of Israel-based machine vision and AI companies by players like Apple and Intel, they are definitely producing the goods.