Good Contents Are Everywhere, But Here, We Deliver The Best of The Best.Please Hold on!
Your address will show here +12 34 56 78

Chatbots are a hot topic in tech at the moment. They’re at the center of a shift in how we communicate, so much so that they are central to the strategy and direction of major tech companies like Microsoft and Facebook. According to Satya Nadella CEO of Microsoft, “Chatbots are the new apps”.

So why exactly have chatbots become so popular?

Their rise in popularity is partly connected to the resurgence of AI and its applications in industry, but it’s also down to our insatiable appetite for on-demand service and our shift to messaging apps over email and phone. A recent study found that 44% of US consumers would prefer to use chatbots over humans for customer relations and 61% of those surveyed said they interact with a chatbot at least once a month. This is because they suit today’s consumers’ needs – they can respond to customer queries instantly, day or night. 

Large brands and tech companies have recognised this shift in customer needs and now rely on messenger and intelligent assistants to provide a better experience for their customers. This is especially true since Facebook opened up its Messenger platform to third-party bots last year.


So while the adoption of intelligent assistants and chatbots is growing at a colossal rate, contrary to popular belief and media hype, they’re actually nothing new. We’ve had them for over fifty years in the Natural Language Processing community and they’re a great example of the core mission of NLP  – programming computers to understand how humans communicate.

In this blog, we’re going to show 3 different chatbots and let you interact with each bot so you can see how they have advanced. We’ll give some slightly technical explanations of how each chatbot works so you can see how NLP works under the hood.

The Chatbots

The three chatbots we’ve gathered on this page are:

  1. ELIZA – a chatbot from 1966 that was the first well-known chatbot in the NLP community
  2. ALICE – a chatbot from the late 1990s that inspired the movie Her
  3. Neuralconvo – a Deep Learning chatbot from 2016 that learned to speak from movie scripts

We should mention here that these three bots are all “chit-chat” bots, as opposed to “task-oriented” bots. Whereas task-oriented bots are built for a specific use like checking if an item is in stock or ordering a pizza, a chit-chat bot has no function other than imitating a real person for you to chat with. By seeing how chit-chat bots have advanced, you’re going to see how the NLP community has used different methods to replicate human communication.

ELIZA – A psycotherapy bot

The first version of ELIZA was finished in 1966 by Joseph Weizenbaum, a brilliant, eccentric MIT professor considered one of the fathers of AI (and who is the subject of a great documentary). ELIZA emulates a psychotherapist, one that Weizenbaum’s colleagues trusted enough to divulge highly personal information, even after they knew it was a computer program. Weizenbaum was so shocked at how his colleagues thought ELIZA could help them, even after they knew it was a computer program, that he spent the rest of his life advocating for social responsibility in AI.


But ELIZA only emulates a psychotherapist because it uses clever ways to return your text as a question, just like a real psychotherapist would. This clever tactic means ELIZA can respond to a question that it doesn’t understand with a relatively simple process of rephrasing the input as a question, so the user is kept in conversation.

Just like any algorithm, chatbots work from rules that tell it how to take an input and produce an output. In the case of chatbots, the input is text you supply to it, and the output is text it returns back to you as a response. Looking at the responses you get from ELIZA, you’ll see two rough categories of rules:

  • On a syntactic level, it transfers personal pronouns (“my” to “your,” and vice versa).
  • To imitate semantic understanding (ie that it understands the meaning of what you are typing), it has been programmed to recognize certain common phrases and keywords and returns phrases that have been marked as suitable returns to these phrases. For instance, if you input “I want to ___” it will return “What would it mean to you if you ___”.

Try and figure out some of ELIZA’s limits for yourself by asking it questions and trying to figure out why it’s returning each of its responses. Remember: it’s from the 1960s, when color televisions were the height of consumer technology.

This is a pure Natural Language Processing approach to building a chatbot: the bot understands human language by the rules mentioned above, which are basically grammar rules programmed into a computer. This achieves impressive results, but if you wanted to make ELIZA more human-like by pure NLP methods you would have to add more and more grammatical rules, and because grammar is complicated and contradictory, you would quickly end up with a sort of “rule spaghetti,” which wouldn’t work. This approach is in contrast with machine learning approaches to chatbots (and natural language in general), where an algorithm will try to guess the correct response based on observations it has made on other conversations. You can see this in action in the final chatbot, Neuralconvo. But first, ALICE.

ALICE – The star of the movie Her

Fast forward from the 1960s to the late 1990s and you meet ALICE, the first well-known chatbot that people could interact with online, and one that developed something of a cult reputation. Director Spike Jonze said that that chatting with ALICE in the early 2000s first put the idea for his 2013 film Her in his mind, a movie where a man falls in love with the AI that powers his operating system.


But just like ELIZA, this is a computer program made up of rules that take an input and produce an output. Under the hood, ALICE is an advance on ELIZA in three respects:

  • it is written in a programming language called Artificial Intelligence Markup Language (AIML), similar to XML, which allows it to choose responses on a more abstract level
  • It contains tens of thousands of possible responses
  • it stores previous conversations with users and adds them to its database.

ALICE is an open source bot, one that anyone can download and modify or contribute to. Written originally by Dr. Richard Wallace, over 500 volunteers have contributed to the bot, creating 100,000s of lines of AIML for ALICE to reproduce in conversation.

So ALICE’s improvements on ELIZA allow for more responses that are better tailored to the text you are supplying it with. This allows ALICE to impersonate a person in general, rather than a therapist specifically. The problem here is that the shortcomings are now more obvious – without open ended statements and questions, the lack of a response that matches your input is more obvious. Explore this for yourself below.

So even though ALICE is a more advanced chatbot than ELIZA, the output responses are still written by people, and algorithms choose which output best suits the input. So essentially, people type out the responses and write the algorithms that choose which of these responses will be returned in the hope of mimicking an actual conversation.

Improving the performance and intelligence of chatbots is a popular research area and much of the recent interest in advancing chatbots has been around Deep Learning. Applying Deep Learning to chatbots seems likely to massively improve a chatbot’s ability to interact more like a human. Whereas ELIZA and ALICE reproduce text that was originally written by a person, a Deep Learning bot creates its own text from scratch, based on human speech it has analyzed.

Neuralconvo – A Deep Learning bot

One such bot is Neuralconvo, a modern Chatbot created in 2016 by Julien Chaumond and Clément Delangue, co-founders of Huggingface, which was trained using Deep Learning. Deep Learning is a method of training computers to learn patterns in data by using deep neural networks. It is enabling huge breakthroughs in computer science, particularly in AI, and more recently NLP. When applied to chatbots, Deep Learning allows programs to select a response or even to generate entirely new text.

Neuralconvo can come up with its own text because it has “learned” by reading thousands of movie scripts and recognizing patterns in the text. So when Neuralconvo reads a sentence it recognizes patterns in your text, refers back to its training to look for similar patterns, and then generates a new sentence for you that it thinks would follow your sentence if it were in the movie scripts in a conversational manner. It’s basically trying to be cool based on movies it’s seen.

The fundamental difference between ELIZA and Neuralconvo is this: whereas ELIZA was programmed to respond to specific keywords in your input with specific responses, Neuralconvo is making guesses based on probabilities it has observed in movie scripts. So there are no rules telling Neuralconvo to respond to a question a certain way, for example, but the possibilities of its answers are limitless.

Considering Neuralconvo is trained on movie scripts, you’ll see that its responses are suitably dramatic.

The exact model that is working under the hood here is based on the Sequence to Sequence architecture, which was first applied to generate dialogue by Quoc Viet Le and Oriol Vinyals. This architecture consists of two parts: the first one encodes your sentence into a vector, which is basically a code that represents the text. After the entire input text has been encoded this way, the second part then decodes that vector and produces the answer word-by-word by predicting each word that is most likely to come next.

Neuralconvo isn’t going to fool you into thinking that it is a person anytime soon, since it is just a demo of a bot trained on movie scripts. But imagine how effective a bot like this could be when trained using context-specific data, like your own SMS or WhatsApp messages. That’s what’s on the horizon for chatbots, but remember – they will still be algorithms taking your text as input, referring to rules, and returning different text as an output.

Well that sums up our lightning tour of chatbots from the 1960s to today. If you’re interested in blogs about technical topics like training AI to play Flappy Bird or why you should open-source your code, take a look at the Research section of our blog, where our research scientists and engineers post about what interests them.

Text Analysis API - Sign up


Extracting insights from millions of articles at once can create a lot of value, since it lets us understand what information thousands of journalists are producing about what’s happening in the world. But extracting accurate insights depends on filtering out noise and finding relevant content. To allow our users access to relevant content, our News API analyzes thousands of news articles in near real-time and categorizes them according to what content is about.

Having content at web-scale arranged into categories provides accurate information about what the media are publishing as the stories emerge. This allows us to do two things, depending on what we want to use the API for: we can either look at a broad picture of what is being covered in the press, or we can carry out a detailed analysis of the coverage about a specific industry, organization, or event.

For this month’s roundup, we decided to do both. First we’re going to take a look at what news categories the media covered the most to see what the content is about in the most written-about categories, and then we’ll pick one category for a more detailed look. First we’ll take a high-level look at sports content, because it’s what the world’s media wrote the most about, and then we’ll dive into stories about finance, to see what insights the News API can produce for us in a business field.

The 100 categories with the highest volume of stories

The range of the subject matter contained in content published every day is staggering, which makes understanding all of this content at scale particularly difficult. However, the ability to classify new content based on well known, industry-standard taxonomies means it can be easily categorized and understood.

Our News API categorizes every article it analyzes according to two taxonomies: Interactive Advertising Bureau’s QAG taxonomy and IPTC’s Newscodes. We chose to use the IAB-QAG taxonomy, which contains just under 400 categories and subcategories, and decided to look into the top 100 categories and subcategories that the media published the most about in June. This left us with just over 1.75 million of the stories that our News API has gathered and analyzed.

Take a look at the most popular ones in the visualization below.

Note: you can interact with all of the visualizations on this blog – click on each data point for more information, and exclude the larger data points if you want to see more detail on the smaller ones.

As you can see, stories about sport accounted for the most stories published in June. It might not surprise people to see that the media publish a lot about sport, but the details you can pick out here are pretty interesting – like the fact that there were more stories about soccer than food, religion, or fashion last month.

The chart below puts the volume of stories about sports into perspective – news outlets published almost 13 times more stories about sports than they did about music.

What people wrote about sports

Knowing that people wrote so much about sport is great, but we still don’t know what people were talking about in all of this content. To find this out, we decided to dive into the stories about sports and see what the content was about – take a look at the chart below showing the most-mentioned sports sub-categories last month.

In this blog we’re only looking into stories that were in the top 100 sub-categories overall, so if your favourite sport isn’t listed below, that means it wasn’t popular enough and you’ll need to query our API for yourself to look into it (sorry, shovel racers).

You can see how soccer dominates the content about sport, even though it’s off-season for every major soccer league. To put this volume in perspective, there were more stories published about soccer than about baseball and basketball combined. Bear in mind, last month saw the MLB Draft and the NBA finals, so it wasn’t exactly a quiet month for either of these sports.

We then analyzed the stories about soccer with the News API’s entities feature to see what people, countries, and organisations people were talking about.

If you check the soccer schedules for June, you’ll see the Confederations Cup is the only major tournament taking place, which is a competition between international teams. However you can see above that the soccer coverage was still dominated by stories about the clubs with the largest fan bases. The most-mentioned clubs above also top the table in a Forbes analysis  f clubs with the greatest social media reach among fans.


So we’ve just taken a look at what people and organizations dominated the coverage in the news categories that the media published the most in. But even though the sports category is the single most popular one, online content is so wide-ranging that sports barely accounted for 10% of the 1.75 million stories our News API crawled last month.

We thought it would be interesting to show you how to use the API to look into business fields and spot a high-level trend in the news content last month. Using the same analysis that we used on sports stories above, we decided to look at stories about finance. Below is a graph of the most-mentioned entities in stories published in June that fell into the finance category.

You can see that the US and American institutions dominate the coverage of the financial news. This is hardly surprising, considering America’s role as the main financial powerhouse in the world. But what sticks out a little here is that the Yen is the only currency entity mentioned, even though Japan isn’t mentioned as much as other countries.

To find out what kind of coverage the Yen was garnering last month, we analyzed the sentiment of the stories with “Yen” in the title to see how many contained positive, negative, or neutral sentiment.

We can see that there is much more negative coverage here than positive coverage, so we can presume that Japan’s currency had some bad news last month, but that leaves with a big question: why was there so much negative press about the Yen last month?

To find out, we used the keywords feature. Analyzing the keywords in stories returns more detailed information than the entities endpoint we used on the soccer content above, so it is best used when you’re diving into a specific topic rather than getting an overview of some news content, since you’ll get a lot of noise then. It is more detailed because whereas the entities feature returns accurate information about the places, people, and organisations mentioned in stories, the keywords feature will also include the most important nouns and verbs in these stories. This means that we can see a more detailed picture of the things that happened.

Take a look below at the most-mentioned keywords from stories that were talking about the Yen last month.

You can see that the keywords feature returns a different kind of result than entities – words like “year,” and “week,” and “investor,” for example. If we looked at the keywords from all of the news content published in June, it would be hard to get insights because the keywords would be so general. But since we’re diving into a defined topic, we can extract some detailed insights about what actually happened.

Looking at the chart above you can probably guess for yourself what the main stories about the Yen last month involved. We can see from the fact that the most-mentioned terms above that keywords like “data,’ “growth,” “GDP,” and “economy” that Japan has had some negative data about economic growth, which explains the high volume of negative stories about the Yen. You can see below how the value of the Yen started a sustained drop in value after June 15th, the day this economic data was announced, and our News API has tracked the continued negative sentiment.

yen to usd

These are just a couple of examples of steps our users take to automatically extract insights from content on subjects that interest them, whether it is for media monitoring, content aggregation, or any of the thousands of use cases our News API facilitates.

If you can think of any categories you’d like to extract information from using the News API, sign up for a free 14-day trial by clicking on the link below (free means free – you don’t need a credit card and there’s no obligation to purchase).

News API - Sign up


Every day, over 100,000 flights carry passengers to and from destinations all around the world, and it’s safe to say air travel brings out a fairly mixed bag of emotions in people. Through social media, customers now have a platform to say exactly what’s on their mind while they are traveling, creating a real-time stream of customer opinion on social networks.

If you follow this blog you’ll know that we regularly use Natural Language Processing to get insights into topical subjects ranging from the US Presidential Election to the Super Bowl ad battle. In this post, we thought it would be interesting to collect and analyze Tweets about airlines to see how passengers use Twitter as a platform to voice their opinion. We wanted to compare how often some of the better known airlines are mentioned by travelers on Twitter, what the general sentiment of those mentions were, and and how people’s sentiment varied when they were talking about different aspects of air travel.

Collecting Tweets

We chose five airlines, gathered 25,000 of the most recent Tweets mentioning them (from Friday, June 9). We chose the most recent Tweets in order to get a snapshot of what people were talking about in Tweets at any given time.


The airlines we chose were:

  1. American Airlines – the largest American airline
  2. Lufthansa – the largest European airline
  3. Ryanair – a low-fares giant that is always courting publicity
  4. United Airlines – an American giant that is always (inadvertently) courting publicity
  5. Aer Lingus – naturally (we’re Irish).


We’ll cover the following analyses:

  • Volume of tweets and mentions
  • Document-Level Sentiment Analysis
  • Aspect-based Sentiment Analysis

Tools used

Sentiment Analysis

Sentiment analysis, also known as opinion mining, allows us to use computers to analyze the sentiment of a piece of text. Essentially analyzing the sentiment of text allows us to get an idea of whether a piece of text is positive, negative or neutral.

For example, below is a chart showing the sentiment of Tweets we gathered that mentioned our target airlines.

This chart shows us a very high-level summary of people’s opinions towards each airline. You can see that the sentiment is generally more negative than positive, particularly in the case of the two US-based carriers, United and American. We can also see that negative Tweets account for a larger share of Ryanair’s Tweets than any other airline. While this gives us a good understanding of the public’s opinion about these certain airlines at the time we collected the tweets, it actually doesn’t tell us much about what exactly people were speaking positively or negatively about.

Aspect-based Sentiment Analysis digs in deeper

So sentiment analysis can tell us what the sentiment of a piece of text is. But text produced by people usually talks about more than one thing and often has more than one sentiment. For example, someone might write that they didn’t like how a car looked but did like how quiet it was, and a document-level sentiment analysis model would just look at the entire document and add up whether the overall sentiment was mostly positive or negative.

This is where Aspect-based Sentiment Analysis comes in, as it goes one step further and analyzes the sentiment attached to each subject mentioned in a piece of text. This is especially valuable since it allows you to extract richer insights about text that might be a bit complicated.

Here’s an example of our Aspect-based Sentiment Analysis demo analyzing the following piece of text: “This car’s engine is as quiet as hell. But the seats are so uncomfortable!”

absa screenshot 1

It’s clear that Aspect-based Sentiment Analysis can provide more granular insight into the polarity of a piece of text but another problem you’ll come across is context. Words mean different things in different contexts – for instance quietness in a car is a good thing, but in a restaurant it usually isn’t – and computers need help understanding that. With this in mind we’ve tailored our Aspect-based Sentiment Analysis feature to recognize aspects in four industries: restaurants, cars, hotels, and airlines.

So while the example above was analyzing the car domain, below is the result of an analysis of a review of a restaurant, specifically the text “It’s as quiet as hell in this restaurant”:

absa screenshot 2

Even though the text was quite similar to the car review, the model recognized that the words expressed a different sentiment because they were mentioned in a different context.

Aspect-based Sentiment Analysis in airlines

Now let’s see what we can find in the Tweets we collected about airlines. In the airlines domain, our endpoint recognizes 10 different aspects that people are likely to mention when talking about their experience with airlines.

absa airlines domain

Before we look at how people felt about each of these aspects, let’s take a look at which aspects they were actually talking about the most.

Noise is a big problem when you’re analyzing social media content. For instance when we analyzed our 25,000 Tweets, we found that almost two thirds had no mention of the aspects we’ve listed above. These Tweets mainly focused on things like online competitions, company marketing material or even jokes about the airlines. When we filtered these noisy Tweets out, we were left with 9,957 Tweets which mentioned one or more aspects.

The chart below shows which of the 10 aspects were mentioned the most.

On one hand it might come as a surprise to see aspects like food and comfort mentioned so infrequently – when you think about people giving out about airlines you tend to think of them complaining about food or the lack of legroom. On the other hand, it’s no real surprise to see aspects like punctuality and staff mentioned so much.

You could speculate that comfort and food are pretty standard across airlines (nobody expects a Michelin-starred airline meal), but punctuality can vary, so people can be let down by this (when your flight is late it’s an unpleasant surprise, which you would be more likely to Tweet about).

What people thought about each airline on key aspects

Now that we know what people were talking about, let’s take a look at how they felt. We’re going to look at how each airline performed on four interesting aspects:

  1. Staff – the most-mentioned aspect;
  2. Punctuality – to see which airline receives the best and worst sentiment for delays;
  3. Food – infrequently mentioned but a central part of the in-flight experience;
  4. Luggage – which airline gets the most Tweets about losing people’s luggage?


We saw in the mentions graph above that people mentioned staff the most when tweeting about an airline. You can see from the graph below that people are highly negative about airline staff in general, with a fairly equal level of negativity towards each airline except Lufthansa, which actually receives more positive sentiment than negative.


People’s second biggest concern was punctuality, and you can see below that the two US-based airlines score particularly bad on this aspect. Also, it’s worth noting that while Ryanair receives very negative sentiment in general, people complain about Ryanair’s punctuality less than any of the other airlines. This isn’t too surprising considering their exemplary punctuality record is one of their major USPs as an airline and something they like to publicize.


We all know airline food isn’t the best, but when we looked at the sentiment about food in the Tweets, we found that people generally weren’t that vocal about their opinions on plane food. Lufthansa receives the most positive sentiment about this aspect, with their pretty impressive culinary efforts paying off. However it’s an entirely different story when it comes to the customer reaction towards United’s food, none of us have ever flown United here in the AYLIEN office, so from the results we got we’re all wondering what they’re feeding their passengers now.


The last aspect that we compared across the airlines was luggage. When you take a look at the sentiment here, you can see that again Lufthansa perform quite well, but in this one Aer Lingus fares pretty badly. Maybe leave your valuables at home next time you fly with Ireland’s national carrier.

Ryanair and Lufthansa compared

So far we’ve shown just four of the 10 aspects our Aspect-based Sentiment Analysis feature analyzes in the airlines domain. To show all of them together, we decided to take two very different airlines and put them side by side to see how people’s opinions on each of them compared.

We picked Ryanair and Lufthansa so you can compare a “no frills” budget airline that focuses on short-haul flights, with a more expensive, higher-end offering and see what people Tweet about each.

First, here’s the sentiment that people showed towards every aspect in Tweets that mention Lufthansa.

Below is the same analysis of Tweets that mention Ryanair.

You can see that people express generally more positive sentiment towards Lufthansa than Ryanair.  This is no real surprise since this is a comparison of a budget airline with a higher-end competitor, and you would expect people’s opinions to differ on things like food and flight experience.

But it’s interesting to note the sentiment was actually pretty similar towards the two core aspects of air travel – punctuality and value.

The most obvious outlier here is the overwhelmingly negative sentiment about entertainment on Ryanair flights, especially since there is no entertainment on Ryanair flights. This spike in negativity was due to an incident involving drunk passengers on a Ryanair flight that was covered by the media on the day we gathered our Tweets, skewing the sentiment in the Tweets we collected. These temporary fluctuations are a problem inherent in looking at snapshot-style data samples, but from a voice-of-the-customer point of view they are certainly something an airline needs to be aware of.

This is just one example of how you can use our Text Analysis API to extract meaning from content at a large scale. If you’d like to use AYLIEN to extract insights from any text you have in mind, click on the image at the end of the post to get free access to the API and start analyzing your data. With the extensive documentation and how-to blogs, as well as detailed tutorials and a great customer support, you’ll have all the help you’ll need to get going in no time!

Text Analysis API - Sign up


For the next instalment of our monthly media roundup using our News API, we thought we’d take a look at the content that was shared most on social media in the month of May. Finding out what content performs well on each social network gives us valuable insights into what media people are consuming and how this varies across different networks. To get these insights, we’re going to take a look at the most-shared content on Facebook, LinkedIn and Reddit.

Together, the stories we analyzed for this post were shared over 10 million times last month. Using the News API, we can easily extract insights about this content in a matter of minutes. With millions of new stories added every month in near real-time, News API users can analyze news content at any scale for whatever topic they want to dig into.

Most Shared Stories on Each Social Network

Before we jump into all of this content, let’s take a quick look at what the top three most-shared stories on each social network were. Take particular note of the style of articles and the subject matter of each article and how they differ across each social network.

Most shared stories on Facebook in May

  1. Drowning Doesn’t Look Like Drowning,” Slate, 1,337,890 shares.
  2. This “All About That Bass” Cover Will Make Every Mom Crack Up,” Popsugar, 913,768 shares.
  3. Why ’80s Babies Are Different Than Other Millennials,” Popsugar, 889,788 shares.


Most shared stories on LinkedIn in May

  1. 10 Ways Smart People Stay Calm,” Huffington Post UK, 8,398 shares.
  2. Pepsi Turns Up The Heat This Summer With Release Of Limited-Edition Pepsi Fire,” PR Newswire, 7,769 shares.
  3. In Just 3 Words, LinkedIn’s CEO Taught a Brilliant Lesson in How to Find Great People,”, 7,389 shares.


Most shared stories on Reddit in May:

  1. Trump revealed highly classified information to Russian foreign minister and ambassador,” The Washington Post, 146,534 upvotes.
  2. Macron wins French presidency by decisive margin over Le Pen,” The Guardian, 115,478 upvotes.
  3. Youtube family who pulled controversial pranks on children lose custody,” The Independent, 101,153 upvotes.


Content Categories

Even from the article titles alone, you can already see there is a difference between the type of stories that do well on each social network. Of course it’s likely you already knew this if you’re active on any of these particular social networks. To start our analysis, we decided to try and quantify this difference by gathering the most-shared stories on each network and categorizing them automatically using our News API to look for particular insights.

From this analysis, you can see a clear difference in the type of content people are more likely to share on each network.


LinkedIn users predictably share a large amount of career-focused content. However, more surprisingly stories which fall into the Society category were also very popular on LinkedIn.

Most-shared stories by category on LinkedIn in May


Reddit is a content-sharing website that has a reputation for being a place where you can find absolutely anything, especially more random, alternative content than you would find on other social media. So it might come as a bit of a surprise to see that over half of the most-shared content on Reddit falls into just two categories, Politics and News.

Most-shared stories by category on Reddit in May


Not surprisingly our analysis, as shown in the pie chart below, shows that almost half of the most-shared stories on Facebook are about either entertainment or food.

Most-shared stories by category on Facebook in May

Note: As a reminder we’ve only analyzed the most shared, liked and upvoted content on each platform.

Topics and Keywords

So far we’ve looked at what categories the most shared stories fall into across each social channel, but we also wanted to dig a little deeper into the topics they discussed in order to understand what content did better on each network. We can do this by extracting keywords, entities and concepts that were mentioned in each story and see which were mentioned most. When we do this, you can see a clear difference between the topics people share on each network.


Below, you can see the keywords from the most shared stories on LinkedIn. These keywords are mostly business-focused, which validates what we found with the categories feature above.

Keywords extracted from the most-shared stories on LinkedIn in May


Likewise with Reddit, you can see below that the keywords validate what the categorization feature found – that most of the content is about politics and news.

Keywords extracted from the most-shared stories on Reddit in May


However on Facebook the most popular content tends to include mentions of family topics, like “father” and “kids,” and “baby” (with the obligatory mentions of “Donald Trump,” of course). This doesn’t correspond with what we found when we looked at what categories the stories belonged to – Arts & Entertainment and Food made up almost 50% of the most-shared content. Take a look below at what keywords appeared most frequently in the most-shared content.

Keywords extracted from the most-shared stories on Facebook in May

In order to find out why there wasn’t as clear a correlation between keywords and categories like we saw on the other platforms, we decided to dive into where this most shared content on Facebook was coming from. Using the source domain feature on the stories endpoint, we found that over 30% of the most shared content was published by one publication – Popsugar. Popsugar, for those who don’t know, is a popular lifestyle media publisher whose content is heavily weighted towards family oriented content with a strong celebrity slant. This means a lot of the content published on Popsugar could be categorized as Arts and Entertainment, while also talking about families.

Most-shared stories by source on Facebook in May

Content Length

After we categorized the stories and analyzed what topics they discuss, we also thought it might be interesting to understand what type of content, long-form or short-form, performs best across each platform. We wanted to see if the length of an article is a good indicator of how content performs on a social network. Our guess was that shorter pieces of content might perform best on Facebook while longer articles would most likely be more popular on LinkedIn. Using the word count feature on the histograms endpoint, it’s extremely easy to understand the the relationship between an article’s popularity and it’s length.

For example, below you can see that the content people shared most on Facebook was usually between 0 and 100 words in length, with people sharing longer posts on LinkedIn and Reddit.

Word count of the most-shared stories on each platform


So to wrap up, we can come to some conclusions about what content people shared in May:

  1. People shared shorter, family-oriented and lighthearted content on Facebook;
  2. Longer, breaking news content involving Donald Trump dominated Reddit;
  3. On LinkedIn, people shared both short and long content that mainly focused on career development and companies.

If you’d like to try the News API out for yourself, click on the image below to start your free 14-day trial, with no credit card required.

News API - Sign up


Artificial Intelligence and Machine Learning play a bigger part in our lives today than most people can imagine. We use intelligent services and applications every day that rely heavily on Machine Learning advances. Voice activation services like Siri or Alexa, image recognition services like Snapchat or Google Image Search, and even self driving cars all rely on the ability of machines to learn and adapt.

If you’re new to Machine Learning, it can be very easy to get bogged down in buzzwords and complex concepts of this dark art. With this in mind, we thought we’d put together a quick introduction to the basics of Machine Learning and how it works.

Note: This post is aimed at newbies – if you know a Bayesian model from a CNN, head on over to the research section of our blog, where you’ll find posts on more advanced subjects.

So what exactly is Machine Learning?

Machine Learning refers to a process that is used to train machines to imitate human intuition – to make decisions without having been told what exactly to do.

Machine Learning is a subfield of computer science, and you’ll find it defined in many ways, but the simplest is probably still Arthur Samuel’s our definition from 1959: “Machine Learning gives computers the ability to learn without being explicitly programmed”. Machine Learning explores how programs, or more specifically algorithms, learn from data and make predictions based on it. These algorithms differ from traditional programs by not relying on strict coded instruction, but by making data-driven, informed predictions or decisions based on sample training inputs. Its applications in the real world are highly varied but the one common element is that every Machine Learning program learns from past experience in order to make predictions in the future.

Machine Learning can be used to process massive amounts of data efficiently, as part of a particular task or problem. It relies on specific representations of data, or “features” in order to recognise something, similar to how when a person sees a cat, they can recognize it from visual features like its shape, its tail length, and its markings, Machine Learning algorithms learn from from patterns and features in data previously analyzed.

Different types of Machine Learning

There are many types of Machine Learning programs or algorithms. The most common ones can be split into three categories or types:

    1. Supervised Machine Learning
    2. Unsupervised Machine Learning
    3. Reinforcement Learning

1. Supervised Machine Learning

Supervised learning refers to how a Machine Learning application has been trained to recognize patterns and features in data. It is “supervised”, meaning it has been trained or taught using correctly labeled (usually by a human) training data.

The way supervised learning works isn’t too different to how we learn as humans. Think of how you teach a child: when a child sees a dog, you point at it and say “Look! A dog!”. What you’re doing here essentially is labelling that animal as a “dog”. Now, It might take a few hundred repetitions, but after a while the child will see another dog somewhere and say “dog,” of their own accord. They do this by recognising the features of a dog and the association of those features with the label “dog” and a supervised Machine Learning model works in much the same way.

It’s easily explained using an everyday example that you have certainly come across. Let’s consider how your email provider catches spam. First, the algorithm used is trained on a dataset or list of thousands of examples of emails that are labelled as “Spam” or “Not spam”. This dataset can be referred to as “training data”. The “training data” allows the algorithm to build up a detailed picture of what a Spam email looks like. After this training process, the algorithm should be able to decide what label (Spam or Not spam) should be assigned to future emails based on what it has learned from the training set. This is a common example of a Classification algorithm – a supervised algorithm trained on pre-labeled data.

Screenshot (58)
Training a spam classifier

2. Unsupervised Machine Learning

Unsupervised learning takes a different approach. As you can probably gather from the name, unsupervised learning algorithms don’t rely on pre-labeled training data to learn. Alternatively, they attempt to recognize patterns and structure in data. These patterns recognized in the data can then be used to make decisions or predictions when new data is introduced to the problem.

Think back to how supervised learning teaches a child how to recognise a dog, by showing it what a dog looks like and assigning the label “dog”. Unsupervised learning is the equivalent to leaving the child to their own devices and not telling them the correct word or label to describe the animal. After a while, they would start to recognize that a lot of animals while similar to each other, have their own characteristics and features meaning they can be grouped together, cats with cats and dogs with dogs. The child has not been told what the correct label is for a cat or dog, but based on the features identified they can make a decision to group similar animals together. An unsupervised model will work in the same way by identifying features, structure and patterns in data which it uses to group or cluster similar data together.

Amazon’s “customers also bought” feature is a good example of unsupervised learning in action. Millions of people buy different combinations of books on Amazon every day, and these transactions provide a huge amount of data on people’s tastes. An unsupervised learning algorithm analyzes this data to find patterns in these transactions, and returns relevant books as suggestions. As trends change or new books are published, people will buy different combinations of books, and the algorithm will adjust its recommendations accordingly, all without needing help from a human. This is an example of a clustering algorithm – an unsupervised algorithm that learns by identifying common groupings of data.

Screenshot (40)
Clustering visualization

Supervised Versus Unsupervised Algorithms

Each of these two methods have their own strengths and weaknesses, and where one should be used over the other is dependent on a number of different factors:
The availability of labelled data to use for training

    Whether the desired outcome is already known
    Whether we have a specific task in mind or we want to make a program for very general use
    Whether the task at hand is resource or time sensitive

Put simply, supervised learning is excellent at tasks where there is a degree of certainty about the potential outcomes, whereas unsupervised learning thrives in situations where the context is more unknown.

In the case of supervised learning algorithms, the range of problems they can solve can be constrained by their reliance on training data, which is often difficult or expensive to obtain. In addition, a supervised algorithm can usually only be used in the context you trained it for. Imagine a food classifier that has only been trained on pictures of hot dogs – sure it might do an excellent job at recognising hotdogs in images, but when it’s shown an image of a pizza all it knows is that that image doesn’t contain a hotdog.

The limits of supervised learning – HBO’s Silicon Valley

Unsupervised learning approaches also have many drawbacks: they are more complex, they need much more computational power, and theoretically they are nowhere near as understood yet as supervised learning. However, more recently they have been at the center of ML research and are often referred to as the next frontier in AI. Unsupervised learning gives machines the ability to learn by themselves, to extract information about the context you put them in, which essentially, is the core challenge of Artificial Intelligence. Compared with supervised learning, unsupervised learning offers a way to teach machines something resembling common sense.

3. Reinforcement Learning

Reinforcement learning is the third approach that you’ll most commonly come across. A reinforcement learning program tries to teach itself accuracy in a task by continually giving itself feedback based on its surroundings, and continually updating its behaviour based on this feedback. Reinforcement learning allows machines to automatically decide how to behave in a particular environment in order to maximize performance based off ‘reward‘ feedback or a reinforcement signal. This approach can only be used in an environment where the program can take signals from its surroundings as positive or negative feedback.

Reinforcement Learning in action

Imagine you’re programming a self-driving car to teach itself to become better at driving. You would program it to understand certain actions – like going off the road for example – is bad by providing negative feedback as a reinforcement signal. The car will then look at data where it went off the road before, and try to avoid similar outcomes. For instance, if the car sees a pattern like when it didn’t slow down at a corner it was more likely to end up driving off the road, but when it slowed down this outcome was less likely, it would slow down at corners more.


So this concludes our introduction to the basics of Machine Learning. We hope it provides you with some grounding as you try to get familiar with some of the more advanced concepts of Machine Learning. If you’re interested in Natural Language Processing and how Machine Learning is used in NLP specifically, keep an eye on our blog as we’re going cover how Machine Learning has been applied to the field. If you want to read some in-depth posts on Machine Learning, Deep Learning, and NLP, check out the research section of our blog.

Text Analysis API - Sign up


Last Friday we witnessed the start of what has been one of the biggest worldwide cyber attacks in history, the WannaCry malware attack. While information security and hacking threats in general receive regular coverage in the news and media, we haven’t seen anything like the coverage around the WannaCry malware attack recently. Not since the Sony Playstation hack in 2011 have we seen as much media interest in a hacking event.

News outlets cover hacking stories quite frequently because they pose this kind of threat to people. However, when we look at the news coverage over the course of the past 12 months in the graph below, we can see that triple the average monthly story volume on malware was produced in the first three days of the attack alone.

In this blog, we’ll use our News API to look at the media coverage of WannaCry before the news of the attack broke and afterwards, as details of the attack began to surface.

Monthly count of articles mentioning “malware” or “ransomware” over the last 12 months

By analyzing the news articles published about WannaCry and malware in general, with the help of some visualizations we’re going to look at three aspects:  

  • warning signs in the content published before the attack;  
  • how the story developed in the first days of the attack;
  • how the story spread across social media channels.


At 8am CET on Friday May 12th, the WannaCry attack began, and by that evening it had infected over 50,000 machines in 70 countries. By the following Monday, that had risen to 213,000 infections, paralyzing computer systems in hospitals, factories, and transport networks as well as personal devices. WannaCry is a ransomware virus – it encrypts all of the data on computers it infects, with users only having their data decrypted after they had paid $300 or $600 ransom to the hackers. Users who have had their device infected can only see the screen below until they have paid the ransom.

WannaCry Screen

Source: CNN Money

In the first six days after the attacks, the hackers have received over USD$90,000 through over 290 payments (you can track the payments made to the known Bitcoin wallets here via a useful Twitter bot created by @collinskeith), which isn’t a fantastic conversion rate considering they managed to infect over 200,000 computers. Perhaps if the hackers had done their market research they would have realized that their target audience – those still using Windows XP – are more likely to still write cheques than pay for things with Bitcoin.

The attack was enabled by tools that exploit security vulnerabilities in Windows called DoublePulsar and EternalBlue. These tools essentially allow someone to access every file on your computer by avoiding the security built into your operating system. The vulnerabilities were originally discovered by the National Security Agency (NSA) in the US, but were leaked by a hacker group called The Shadow Brokers in early April 2017.

The graph below, generated using the time series feature in our News API, shows how the coverage of ransomware and malware in articles developed over time. The Shadow Brokers’ dump in early April was reported on and certainly created a bit of noise, however it seems this was forgotten or overlooked by almost everyone until the attack itself was launched. The graph then shows the huge spike in news coverage once the WannaCry attack was launched.

Volume of articles mentioning “malware” or “ransomware” in April and May

Monitoring the Media for Warning Signs

Since WannaCry took the world by such surprise, we thought we’d dig into the news content in the weeks prior to the attack and see if we could find any signal in the noise that would have alerted us to a threat. Hindsight is 20/20, but an effective media monitoring strategy can give an in-depth insight into threats and crises as they emerge.

By simply creating a list of the hacking tools dumped online in early April and tracking mentions of these tools, we see definite warning signs. Of these 30 or so exploits, DoublePulsar and EternalBlue were the only ones mentioned again before the attack, and these ended up being the ones used to enable the WannaCry attack.

Mentions of each of the exploit tools dumped in April and May


We can then use the stories endpoint to collect the articles that contributed to the second spike in story volumes, around April 25th. Digging into these articles provides another clear warning: the articles collected cover reports by security analysts estimating that DoublePulsar had been installed on 183,000 machines since the dump ten days earlier (not too far off the over 200,000 machines WannaCry has infected). Although these reports were published in cybersecurity publications, news on the threat didn’t make it to mainstream media until the NHS was hacked and hospitals had to send patients home.

DoublePulsar article

Story on the spread of DoublePulsar and EternalBlue in SC Magazine

Trends in the Coverage

As it emerged early on Friday morning that malware was spreading through personal computers, private companies and government organizations, media outlets broke the story to the world as they gained information. Using the trends endpoint of our News API, we decided it would be interesting to try and understand what organizations and companies were mentioned in the news alongside the WannaCry attack. Below you can see the most mentioned organisations that were extracted from news articles about the attack.

Organisations mentioned in WannaCry stories

The next thing we wanted to do was to try and understand how the story developed over time and to illustrate how the media focus shifted from “what,” to “how,” to “who” over a period of a few days.

The focus on Friday was on the immediate impact on the first targets, like the NHS and Telefonica, but as the weekend progressed the stories began to focus on the method of attack, with many mentions of Windows and Windows XP (the operating system that was particularly vulnerable). On Monday and Tuesday the media turned then their focus to who exactly was responsible and as you can see from the visualization below mentions of  North Korea, Europol, and the NSA began to surface in the news stories collected.
Take a look at the chart below to see how the coverage of the entities changed over time.


Mentions of organisations on WannaCry stories published from Friday to Tuesday


Most Shared Stories about WannaCry

The final aspect of the story as a whole we focused on was how news of the threat spread across different social channels. Using the stories endpoint, we can rank WannaCry stories by their share counts across social media to get an understanding into what people shared about WannaCry. We can see below that people were very interested in the young man who unintentionally found a way to prevent the malware from attacking the machines it installed itself on. This contrasts quite a bit with the type of sources and subject matter of the articles from before the attack began.



  1. The 22-year-old who saved the world from a malware virus has been named,” Business Insider. 33,800 shares.
  2. ‘Accidental hero’ finds kill switch to stop spread of ransomware cyber-attack,” 28,420 shares.
  3. Massive ransomware attack hits 99 countries,” CNN. 13,651 shares.



  1. A Massive Ransomware ‘Explosion’ Is Hitting Targets All Over the World,” VICE Motherboard. 3,612 shares.
  2. Massive ransomware attack hits 99 countries,” CNN. 2,963 shares.
  3. Massive ransomware attack hits 74 countries,” CNN. 2,656 shares.



  1. ‘Accidental hero’ finds kill switch to stop spread of ransomware cyber-attack,” 24,497 upvotes.
  2. WannaCrypt ransomware: Microsoft issues emergency patch for Windows XP,” ZDNet. 4,454 upvotes.
  3. Microsoft criticizes governments for stockpiling cyber weapons, says attack is ‘wake-up call’” CNBC. 3,403 upvotes.

This was a quick analysis of the media reaction to the WannaCry attack using our News API. If you’d like to try it for yourself you can create your free account and start collecting and analyzing stories. Our News API is the most powerful way of searching, sourcing, and indexing news content from across the globe. We crawl and index thousands of news sources every day and analyze their content using our NLP-powered Text Analysis Engine to give you an enriched and flexible news data source.

News API - Sign up


Last month was full of unexpected high-profile publicity disasters, from passengers being dragged off planes to Kendall Jenner failing to solve political unrest.  For this month’s Monthly Media Roundup we decided to collect and analyze news stories related to three major events and try to understand the media reaction to each story, while also uncovering the impact this coverage had on the brands involved.

In the roundup of the month’s news, we’ll cover three major events:

  1. United Airlines’ mishandling of negative public sentiment cut their market value by $255 million.
  2. Pepsi’s ad capitalizing on social movements shows the limits of appealing to people’s consciousness for advertising.
  3. The firing of Bill O’Reilly shows how brands have become aware of the importance of online sentiment.

1: United Airlines

On Monday, April 10th, a video went viral showing a passenger being violently dragged off a United Airlines flight. On the same day, United CEO Oscar Munoz attempted to play down the controversy by defending staff and calling the passenger “disruptive and belligerent”. With investors balking at the tsunami of negative publicity that was compounded by Munoz, the following day United’s share price fell by over 1%, shaving $255 million off their market capitalization by the end of trading.
We collected relevant news articles published in April using a detailed search query with our News API. By analyzing the volume of articles we collected and
analyzing the sentiment of each article, we were able to get a clear picture of how the media responded to the video and subsequent events:

Media Reaction to United Airlines Controversy

The volume of stories published shows how quickly the media jumped on the story (and also that Munoz’s statement compounded the issue), while the sentiment graph shows just how negative all that coverage was. The key point here is that the action United took in dealing with the wave of negative online sentiment – not listening to the customer – led to their stock tumbling. Investors predicted that ongoing negative sentiment on such a scale would lose potential customers, and began offloading shares in response.

Most shared stories about United in April


  1. United Airlines Stock Drops $1.4 Billion After Passenger-Removal Controversy” – Fortune, 57,075 shares
  2. United Airlines says controversial flight was not overbooked; CEO apologizes again” – USA Today, 43,044 shares


  1. United Airlines Passenger Is Dragged From an Overbooked Flight” – The New York Times, 1,443 shares
  2. When a ticket is not enough: United Airlines forcibly removes a man from an overbooked flight” – The Economist, 1,430 shares


  1. Simon the giant rabbit, destined to be world’s biggest, dies on United Airlines flight” – Fox News, 62,830 upvotes
  2. Passengers film moment police drag man off a United Airlines plane” – Daily Mail, 25,142 upvotes

2: Pepsi

In contrast with United’s response, Pepsi’s quick reaction to online opinion paid off this month as they faced their own PR crisis. On April 3rd, Pepsi released an ad that was immediately panned for trying to incorporate social movements like Black Lives Matter into a soft drink commercial that prompted ridicule online.
After using our News API to collect every available article on this story and analyzing the sentiment of each article, we can get a picture of how the media reacted to the ad. This lets us see that on the day after the ad was launched, there were over three times more negative articles mentioning Pepsi than positive ones.

Media Reaction to Pepsi’s Kendall Jenner Ad

As a company that spends $2.5 billion annually on advertising, Pepsi were predictably swift in their response to bad publicity, pulling the ad from broadcast just over 24 hours after it was first aired.

Even though this controversy involved a major celebrity, the Pepsi ad debacle has actually been shared significantly less compared to the other PR disasters. By using our News API to rank the most shared articles across major social media platforms, we can see that the story gathered a lot less pace than those covering the United scandal.

Most shared articles about Pepsi in April


  1. Twitter takes Pepsi to task over tone-deaf Kendall Jenner ad” – USA Today, 19,028 shares
  1. Hey Pepsi, Here’s How It’s Done. Heineken Takes On Our Differences, and Nails It” – AdWeek, 16,465 shares


  1. Heineken Just Put Out The Antidote to That Pepsi Kendall Jenner Ad” – Fast Company, 1,833 shares
  2. Pepsi Just Released An Ad That May Be One Of The Worst Ads Ever Made (And That’s Saying Something)” –, 1,192 shares


  1. Pepsi ad review: A scene-by-scene dissection of possibly the worst commercial of all time” – Independent UK, 58 upvotes
  2. Pepsi pulls Kendall Jenner advert amid outcry” – BBC, 58 upvotes

3: Fox Firing Bill O’Reilly

On April 1st, the New York Times published an article detailing numerous previously unknown sexual harassment cases brought against Bill O’Reilly. O’Reilly, who was Fox’s most popular host, drew an average of 3 million views to his prime-time slot. Though his ratings were unscathed (they actually rose), advertisers began pulling their ads from O’Reilly’s slot in response to the negative PR the host was receiving.
We sourced every available story about Bill O’Reilly published in April, and analyzed the sentiment of each article. Below we can see just how negative this coverage was over the course of the story.

Media Reaction to Bill O’Reilly Controversy

This was not the first time that O’Reilly had been accused of sexual harassment, having been placed on leave in 2004 for the same reason. In both 2004 and April 2017, O’Reilly’s viewer ratings remained unhurt by the scandals. What is different in 2017 is that brands are far more aware of the “Voice of the Customer” – social media and online content representing the intentions of potential customers. This means negative coverage and trends like #DropOReilly have a considerable effect on brands’ marketing behaviour.

Most-mentioned Keywords in Articles about Bill O’Reilly in April

By analyzing the content from every article about Bill O’Reilly in April, we can rank the most frequently used Entities and Keywords across the collection of articles. Not surprisingly, our results show us that the coverage was dominated by the topic of sexual harassment and Fox News. But our analysis also uncovered other individuals and brands that were mentioned in news articles as being tied to the scandal. Brands like BMW and Mercedes took swift action to distance themselves from the backlash by announcing they were pulling advertising from O’Reilly’s show in an attempt to preempt any negative press.

Most shared articles about Bill O’Reilly in April


  1. Bill O’Reilly is officially out at Fox News” – The Washington Post, 63,341 shares
  2. Bill O’Reilly Is Out At Fox News” – NPR, 50,895 shares


  1. Bill O’Reilly Out At Fox News” – Forbes, 861 shares
  2. Fox Is Preparing to Cut Ties with Bill O’Reilly” – The Wall Street Journal, 608 shares


  1. Sources: Fox News Has Decided Bill O’Reilly Has to Go” – New York Magazine, 80,436 upvotes
  2. Fox News drops Bill O’Reilly in wake of harassment allegations” – Fox News, 12,387 upvotes

We hope this post has given you an idea of the importance of media monitoring and content analysis is from a PR and branding point of view. Being able to collect and understand thousands of articles in a matter of minutes means you can quickly assess media reaction to PR crises as they unfold.

Ready to try the News API for yourself? Simply click the image below to sign up for a 14-day free trial.

News API - Sign up


Screen Shot 2017-04-25 at 19.38.39










2017 looks set to be a big year for us here on Ormond Quay – with AYLIEN in hyper-growth mode, we’ve added six new team members in the first four months of the year, and that looks set to continue. After this period of quick growth, we thought we’d take stock and introduce you to the newest recruits.

Say hello to our newest recruits!


Mahdi: NLP Research Engineer

From Qom, Iran, Mahdi became an open-source contributor at age 16, working on Firefox Developer Tools and other projects you can find on his GitHub. At 18, he was hired as a full-stack developer to work on browser extensions and mobile apps. Mahdi just started at AYLIEN as a Natural Language Processing Research Engineer focusing on Deep Learning, while also working as a full-stack developer on our web apps. He blogs about programming (and life in general) on

Mahdi is a serious outdoorsman who can be found hiking in the hills and practicing Primitive Living. He also loves learning languages and reading, which provides him with the raw material to fill our Slack loading messages with some supremely inspirational quotes!


Demian: NLP Research Intern

Demian comes from Braunschweig in Central Germany and has completed a degree in Computational Linguistics in the University of Heidelberg. As part of his degree he studied NLP and Artificial Intelligence in information extraction, and he is already familiar with Dublin from an Erasmus year spent in Trinity College. Demian previously worked in the Forensic Department of PwC in Germany, and here at AYLIEN he is going to research document summarization and event extraction for our News API.

Besides being a proficient coder, Demian is an avid painter and reader, and can be found running in Dublin’s parks.


Sylver: Data Management Intern

Growing up between Dublin and Seattle, Sylver swapped one rainy town with a thriving tech scene for another. She is currently studying Legal Practice and Procedures, and before starting with us here at AYLIEN she was an editor of everything from novels to academic papers. Here at AYLIEN Sylver works on maintaining and managing our datasets and models.

A previous owner of 10 snakes (at the same time), Sylver spends her spare time caring for exotic pets, and is interested in reading, alternative modelling, and fitness.


Hosein: Web Designer

Hosein is a native of Tehran who has three years’ experience in UI design and front-end development, having worked with startups and IT companies in Iran. A newcomer to NLP, Hosein is designing the AYLIEN website and web apps, and also developing our front-ends.

While he’s away from his laptop, Hosein is usually out taking photographs and finding out more about cameras.


Erfan: NLP Research Engineer

From Urmia in Northwestern Iran, Erfan holds a Bachelor’s Degree in Software Engineering from Sharif University in Tehran. He has been researching computer vision for three years and you can read about his research on his blog. For his thesis, he used Deep Neural Nets to study the joint embedding of image and text, and at AYLIEN he is going to research and work on using memory-augmented neural nets, focusing on question-answering.


Will: Content Marketing Intern

From the comparatively less exotic background of Dublin, Will is a Classics graduate who completed a Master’s in Digital Humanities at Trinity College, where he was introduced to NLP when he tried to write some code to index where authors use Latin words across English Literature. At AYLIEN, he is joining the Sales, Marketing, and Customer Success team to bolster our content creation and distribution efforts, and is even writing this exact sentence at this very moment in time.

Outside of AYLIEN, Will is an avid reader and learner of languages, and when he’s outside, he can be found running or hiking.

Come work with us!

So that sums up our new recruits – a pretty diverse group who all gravitated towards languages and programming. If you think you’d like to join us, take a look at, email us at, or call in for a fresh cup of coffee. We’re always interested in talking to anyone working on or studying NLP, Computational Linguistics or Machine Learning.

News API - Sign up