In our blog on the Digital Disruption at The New York Times, we delved into their leaked innovation report where Times’ staffers identified the need to attract readers by recommending older stories, suggesting related stories, and better packaging and personalizing content to individual readers' interests. The report especially emphasizes the need to reach readers where they are, which is often on their mobile devices.
Well, here’s the Hot News Timemachine, a Chrome browser extension that demonstrates a little of what you can do by accurately tagging your news archives with high-level, human-like concepts. It’s by a couple of Aussies, Kenni Bawden and James Edwards, who developed it for the GovHack2014 Hackerfest.
Here’s what the creators say about it:
"Hot News Timemachine is a fun, new Google Chrome browser extension that shows you that anything new in the news, is really old news, and provides you with a serendipitous and intriguing alternative to today's click-bait fluff."
"When you click on an Aussie web news story, the Hot News Timemachine roars into action! Hot News Timemachine swaps out the boring, current 'news' story that you are reading for a much more interesting, old fashioned one."
Technical Details: Hot News Timemachine was created by Kenni Bawden and James Edwards, for the GovHack2014 Hackerfest. It utilizes AlchemyAPI's Concept Tagging engine to process the extensive, digitized collection of Australian newspapers found on Trove, and provides a link to additional relevant insights gleaned from the Humanities Networked Infrastructure (HuNI) collection.
Give it a try and share your thoughts on how repackaging "old news" can generate new revenues.
By Marissa Kaufmann
Recently at MozCon 2014 - a gathering of digital marketers - Zeph Snapp, CEO and Founder of Altura Interactive presented his tips for leveraging existing content in other languages. Attendees learned how to go beyond the technical implications of international SEO and the benefits of using natural language processing tools to perform tasks such as pinpoint keywords and map them to specific pages. We got together with Zeph to learn a bit more about his presentation and how he uses AlchemyAPI to improve international SEO.
1. Tell us about your presentation at MozCon.
My presentation was about localizing content for international audiences. You can download the slides or watch the presentation here. I want to teach digital marketers in the U.S. how to plan for and distribute content for audiences in other languages. There are so many tools out there that can make digital marketers’ and SEO experts’ lives easier. I wanted to share the resources, like AlchemyAPI, that I’ve found so that others could benefit.
2. In your presentation, you discussed how you incorporate AlchemyAPI in your work. Tell us how you use natural language processing as a digital marketer.
At Altura Interactive, we use AlchemyAPI’s Entity Extraction API to help our translators understand the entities they should translate and the ones that should continue untouched. We also use the Keyword Extraction API to map keywords to specific pages. These services help us enrich our content by providing relevance scores and sentiment analysis for terms, among other things. And, we use the Language Detection API to divide the backlinks, or incoming links, to a specific page so that we can analyze them and reach out to websites that are in other languages to ask them to point their links to the appropriate corresponding pages.
3. What are your tips for others who are just getting started?
Before using AlchemyAPI, you and your resident app developer need to read this guide to getting started with AlchemyAPI, and then go to town!
4. How did you find out about AlchemyAPI?
At a conference, a colleague and I were talking about how we could leverage natural language processing and your team came up.
5. Where do you see unstructured data analysis heading in the future?
Some of the most interesting and important work for digital and SEO marketers is analyzing language at a massive scale. It will be important for NLP technology to understand the connotations of specific words in specific contexts. The implications of NLP tools for SEO and building international content are amazing. I’m excited to see solutions, like AlchemyAPI, used more and more by professionals in my field.
For most of us, summer means backyard BBQs, beach vacations, and relaxing by the pool. While our team members at AlchemyAPI have gotten their fill of hamburgers and potato salad, we have also been busy adding new features to our natural language processing (NLP) and computer vision solutions.
With AlchemyAPI’s NLP and computer vision APIs, customers gain access to updates the moment they become available. Read on to learn about the most recent enhancements and visit our Recent Updates timeline for additional information.
1. Combined Call
With AlchemyAPI’s combined call, you are able to analyze a single piece of content (URL, HTML, Text) with multiple text and image analysis features all at once.
2. Publication Date API
This newly available API helps you group articles by extracting publication date information from web pages and normalizing the data to give you standardized formats. This API solves the problem of determining a publication date when faced with the following challenges: varied date formats (05/10/2014 or May 10th, 2014 or the 10th of May 2014), placement on the webpage (header, h1, main body), and differentiating between multiple dates on a page.
Publication date extraction combined with other text analysis features enables the generation of tag clouds, sentiment towards specific topics, and more on a temporal basis.
3. New Web Page Cleaning
The new web page cleaning system is more accurate, has increased precision on extracting the main text from article pages, and recognizes a wider variety of types of pages and handles them appropriately. Overall, the new system makes text extraction results more meaningful and is able to extract data from a larger percentage of pages than was previously possible.
4. Constituent Parser
The constituent parser builds a parse tree from the words in a sentence. Parse trees are useful structures to show the relationship between words. For example, in the phrase "new cars and trucks," we know that the word "new" applies to both cars and trucks. AlchemyAPI's technology understands the structure of complex sentences and we are now exposing this powerful process to customers.
5. New Taxonomy
In Q2, we rolled out a significant taxonomy update, offering improved accuracy on web page content analysis. Confidence scores have been improved to more accurately convey when the results can be trusted. If you need custom categories, we can help with that, too. Contact us for more information.
6. Hashtag Sentiment
Sentiment hashtag decomposition makes it possible to determine the sentiment of hashtags by splitting them into individual words and commonly used phrases.
Given any URL, the Image Link Extraction API will scan the designated page to find the most prominent image and directly retrieve the URL for that image. It can then be appropriately classified and tagged. You can use the Image Link Extraction API to aggregate images and understand the context in which they are being served.
With the Image Tagging API, you can quickly categorize and organize image libraries at a massive scale. By understanding complex visual scenes in their broader context, you can automatically extract knowledge from images and act upon what you learn.
Timely, relevant, and accurate information is a powerful ally for news organizations reporting on natural disasters or humanitarian conflicts. However, gathering that data can be quite the challenge. CrisisNET, an Ushahidi initiative, is solving this problem for journalists, data scientists, and developers with a platform that provides easy access to critical information.
CrisisNET partners with AlchemyAPI to pull in data from thousands of data sources, from individual Facebook posts to UNHCR refugee updates to Ushahidi deployments. The multi-source data is first normalized into a common structure and then enhanced with relevant metadata (e.g. language, reverse geo-coding, and keywords). In minutes, this data can be made available to users through multiple API endpoints.
Imagine you want to understand the content of photos being posted about the current Iraq conflict. Specifically, you want images containing reported members from the Islamic extremist group, ISIS. Can you name the hundreds of data sources sharing information on Iraq and find the related images? If not, that means you’ll have to spend days scouring the internet for sources, scanning each one, looking for relevant photos (excluding advertisements or propaganda), and tagging each image by hand. That is a lot of work.
As the CrisisNET team shares in this case study, they leverage AlchemyAPI’s image recognition technology to dig deeper into the data stream. With AlchemyAPI, CrisisNET understands the complex content of images, extracts ISIS-related photos, and identifies images of people. By using this technology to filter the data collected, CrisisNET was able to quickly document the current situation and share the timeline below in the “Faces of ISIS” blog post.
While unstructured data analysis and natural language understanding are often used by enterprises who want to learn about their customers, instances also exist in which these services are used to benefit society as a whole. “By removing barriers to accessing and analyzing unstructured information, we are hoping to impassion people who otherwise might not get involved and help drive local and global changes,” shares Jonathan Morgan, Co-founder and Technical Director at CrisisNET.
When we talk about company culture at AlchemyAPI, we are not referring to the perks we offer like company-paid, twice daily Starbucks runs, team lunch on Fridays, gym memberships, bike rentals, or the breathtakingly awesome downtown office view. We are talking about the people who work at AlchemyAPI and the way they define who we are as a company.
Computer science geniuses, modern marketers and sales extraordinaires comprise our energetic team focused on delivering the latest and greatest artificial intelligence capabilities for our customers. Our team members describe us as “clever, inclusive, intellectual, entertaining, innovative, kind, and fun.” Working here offers a challenge, but we conquer it while wearing flip flops and drinking our grande triple lattes.
Colby Toland, engineer at AlchemyAPI says, “It's easy to get bored as an engineer. Eventually you master whatever you are working on. Here, I can count on the complexity of the problem to keep me exploring new knowledge every day.”
At AlchemyAPI, we seek to provide a comfortable environment for employees to grow personally, mentally, and professionally. “The majority of the work we do is on the cutting edge of what is possible,” shares Aaron Chavez, Chief Scientist.
By the way, we are always looking for smart people who want to work at a place where innovation is valued, everyone has a voice and team “bonding” happens naturally. Check out our open positions and let us know how you could make a difference as a part of our team.
Learn more about AlchemyAPI:
Determining Twitter sentiment is already a challenge given the casual nature of tweets and the limits of 140 characters. But then you add hashtags: unspaced words or phrases with a ‘#’ sign in front of them. We look at a hashtag and are naturally able to break it down into the most probable set of words or phrases. A computer instead sees a hashtag as a compound word. It must first perform “sentiment hashtag decomposition” to break the hashtag into recognizable words and phrases before it can determine its sentiment.
How is hashtag sentiment performed? Instead of using the inefficient process of compiling a list of commonly used hashtags, the process of “compound word expansion” separates the hashtag into individual words using statistical language modeling. The hashtag, or the “compound word,” is “expanded” by adding spaces between what the system has determined is the most probable sequence of words. Since there are often multiple ways to chunk words in a hashtag, statistical language modeling helps to provide the most probable one.
For example, the hashtag #daretodream is a hopeful and positive statement used in a variety of ways, but adopted lately by World Cup fans. Our natural language processing (NLP) system recognizes #daretodream as a positive statement, and when it’s added to a neutral statement such as “Room on the bench for these lads?” the system realizes that it’s now a positive one (try it yourself using our online demo). Without Twitter hashtag decomposition the computer would see #daretodream as “daretodream,” instead of “dare to dream.”
— Sunderland AFC (@SAFCofficial) March 2, 2014
Another example is the neutral statement, “Me and my bros after training,” made positive by these hashtags: #dfb #Brazil #WorldCup2014 #team #friends #training #poldi #aha #lp10 #germany.
One more example, the title of this post, “Twitter Hashtag Sentiment Is Easy #ExceptWhenItsNot” is found to be overall neutral however #ExceptWhenItsNot is correctly found to be negative.
To read more about the process of applying sentiment analysis to Twitter using AlchemyAPI, read developer Greg Case’s blog post on “Applying Sentiment Analysis to Twitter.”
Is beauty really in the eye of the beholder? Richard Shaw, AlchemyAPI user, isn’t so sure. Recently, Shaw put this classic statement to the test by analyzing 1000 pictures that were tagged #beautiful on Instagram.
A person could spend hours trying to view and classify 1000 images by hand. And oftentimes, the results are not accurate or consistent enough to draw good conclusions. Shaw completed tagging his library of images in seconds using a free trial of computer vision API, AlchemyVision, to determine patterns in the data.
Shaw continued his experiment by looking for patterns in image color and composition for certain tags, creating a network graph to show relationship, and plotting common properties of #beautiful people to draw his conclusions. As for the question, is “beauty in the eye of the beholder”? Shaw’s experiment presumes that if it is, certain people in certain places are surely looking through the same eye.
Smart machines, known for completing human-like tasks with astounding accuracy and speed, continue to gain popularity as enterprises and tech enthusiasts employ them to solve unique and complex data problems. These systems are helping the world's data-minded predict the spread of diseases, tag expansive image libraries, monitor social media posts for brand sentiment and so much more. But, smart machines aren't just 'cool'. Savvy developers use them to power their applications because they are effective.
Recently, Gartner, a technology and research firm, named AlchemyAPI to its “Cool Vendors in Smart Machines 2014” report.* AlchemyAPI joins a short list of leaders for its wide range of scalable AI services that help both enterprises and independent software vendors build unstructured data applications.
“We are honored to be recognized for our commitment and passion to make deep learning systems available to companies of all shapes and sizes. We’re excited to be at the forefront of the industry and look forward to the explosion of AI-powered applications coming in the near future,” shares Elliot Turner, CEO & Founder of AlchemyAPI.
Learn more with these resources:
* Gartner Inc., "Cool Vendors in Smart Machines 2014" by Tom Austin, Alexander Linden, Carol Rozwell, Kenneth F. Brant, Adib Carl Ghubril, Anurag Gupta; April 11, 2014.
The notion of artificial intelligence and smart machines may bring to mind images of apocalyptic robots, Skynet or love affairs with operating systems. But the AI we see today looks a lot different in real life and takes the form of smarter applications you probably use everyday like the U.S. Mail, Siri, GoogleNow, Pandora and Netflix.
AI is here – it’s just not what you thought it would be.
So, how do you take technology that traditionally takes thousands of Ph.D hours to create and tune, and make it useful in your own applications, for cheap? Use REST calls. Here’s how.
Here’s an example of how you can offload AI’s expensive computational work and still realize all of AI’s benefits. In this scenario, an application developer is building a voice and visual assistant that allows hikers around the world to understand the things they see on their adventures.
Let’s look at steps the application developer might take:
Whether you want to use computer vision to find each and every moment Leonardo DiCaprio appears in a film, machine learning to identify fraudulent account activity, or sentiment analysis to track how people feel about your products – numerous forms of artificial intelligence are already available for you to use in your apps. And it’s now easier than ever.
Recently, Binghamton University students, Lucas Eager Leavitt and Yuval Shafir participated in the Bitcamp Hackathon at the University of Maryland. After 36 hours of intense work, Mooddio was unveiled. The music player application built with AlchemyAPI’s Sentiment Analysis API, along with a couple others, creates playlists for users based on the emotions conveyed by recent tweets. If you are tweeting about how relaxing your beach vacation is, you're likely to get an easy listening playlist, whereas if you're tweeting about poor customer service, you're going to get more tormented tunes throughout the day. Here’s what Leavitt and Shafir had to say about their app.
1. Tell us about your app. What does it do? How did you come up with the idea?
Mooddio is a music player web app that uses the sentiment of your tweets to build mood-based playlists. The user inputs their Twitter handle and the app begins playing music that is classified as happy, excited, sad, angry or relaxing, based on the sentiment of their most recent tweets. We came up with the idea for Mooddio on the bus ride to Bitcamp after trying to think of a simpler way to find music that suits your emotional state at any given moment.
2. Which feature of AlchemyAPI did you use? Which APIs did you use? Which programming language is the app written in?
3. What are your plans for Mooddio?
We are planning to add a few features and deploy the app for public use in the coming weeks. We received a lot of encouraging feedback at Bitcamp and quite a few suggestions to enhance our app. We are excited to continue working on it.
4. Who was on the team?
Lucas Eager Leavitt: I am a sophomore Computer Science major at Binghamton University. I began programming during my sophomore year of high school. This is the first year I’ve been involved in the hacker scene. Bitcamp was my second hackathon and my first experience in building a larger scale web app while working with multiple APIs.
Yuval Shafir: I am a sophomore majoring in Computer Science. I began programming just one month before entering Binghamton University. I am also a co-founders of HackBU and we recently hosted BU’s first hackathon. I first learned about AlchemyAPI at MHacks and quickly knew that I wanted to use it in a future project. When we came up with the idea for Mooddio, I realized that AlchemyAPI would perfectly fit our needs.
5. Anything else you'd like to add?
We are excited to share that Mooddio was selected for "Best Use of the Rdio API" at Bitcamp and was featured as a top startup idea in the InTheCapital article, “11 Awesome Startup Ideas Launched in Just 36 Hours at UMD”. Thanks to AlchemyAPI for the amazing sentiment analysis tool. It was the heart of our app and made it all possible.