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Introduction

Welcome to the second installment in our series of monthly posts where we’ll be showcasing our News API by looking back at online news stories, articles and blog posts to uncover emerging insights and trends from topical categories.

For our February review, we looked at three IAB categories: Arts & Entertainment, Science and Politics.

For March, we’ve decided to narrow our focus a little further by looking at IAB subcategories to give you an idea of just how specific and granular you can be when sourcing and analyzing content through the News API. With this in mind, we’ve gone with the following three subcategories:

  1. Cell phones (subcategory of Tech & Computing)
  2. Boxing (subcategory of Sports)
  3. Stocks (subcategory of Personal FInance)

and for each subcategory we have performed the following analysis;

  • Publication volumes over time
  • Top stories
  • Most mentioned topics
  • Most shared stories on social media

Try it yourself

We’ve included code snippets for each of the analyses above so you can follow along or modify to create your own search queries.

If you haven’t already signed up to our News API you can do so here with a free 14 day News API trial.

1. Cell phones

The graph below shows publication volumes in the Cell phones subcategory throughout the month of March 2017.

Note: All visualizations are interactive. SImply hover your cursor over each to explore the various data points and information.

Volume of stories published: Cell phones

From the graph above we can see a number of spikes indicating sharp rises in publication volumes. Let’s take a look at the top 3;

Top stories

The three stories that contributed to the biggest spikes in news publication volumes;

  1. Samsung release their latest flagship phone, the Galaxy S8.
  2. The UK introduces a loss-of-license punishment for new drivers caught using their cell phones while driving.
  3. HTC reveal a limited edition version of their U Ultra smart phone.

It will perhaps come as no surprise to see one of the world’s top smartphone manufacturers, Samsung, getting the most media attention with the launch of their latest flagship model. In comparison, rivals HTC failed to generate the same level of hype around their latest model. However, by releasing a teaser about a surprise product release on March 15 they still managed to generate two of the top four publication volume spikes within the cell phone category in March.

Try it yourself – here’s the query we used for volume by category

Read more: We looked at Samsung’s recent exploding battery crisis to highlight how news content can be analyzed to track the voice of the customer in relation to crisis prevention and damage limitation.

Most mentioned topics

From the 7,000+ articles we sourced from the Cell phones category in March we looked at the most mentioned topics;

Try it yourself – here’s the query we used for most mentioned topics

Most shared on social media

What were the most shared stories on social media? We analyzed share counts from Facebook, LinkedIn and Reddit to see what type of content is performing best on each channel.

Facebook

  1. Man dies charging iPhone while in the bath (BBC. 26,072 shares)
  2. US bans electronic devices on flights from eight Muslim countries (The Independent. 25,886 shares)

Linkedin

  1. Samsung tries to reclaim its reputation with the Galaxy S8 (Washington Post. 890 shares)
  2. It’s Possible to Hack a Phone With Sound Waves, Researchers Show (NY Times. 814 shares)

Reddit

  1. Samsung confirms the Note 7 is coming back as a refurbished device (The Verge. 7,193 votes)
  2. The Galaxy S8 will be Samsung’s biggest test ever (The Verge. 4,981 votes)

Try it yourself – here’s the query we used for social shares

2. Boxing

We sourced a total of 9,000+ articles categorized under Boxing and found that what goes on outside the ring can garner just as much (if not more) media interest than what happens in it.

Volume of stories published: Boxing

Top stories

The three stories that contributed to the biggest spikes in news publication volumes;

  1. Heavyweight bout between David Haye and Tony Bellew.
  2. Floyd Mayweather urges the UFC to allow him and Conor McGregor to fight.
  3. Middleweight bout between Gennady Golovkin and Daniel Jacobs.

The two biggest fights in world boxing during the month of March are clearly represented by publication spikes in the chart above, particularly the heavyweight clash between Haye and Bellew. However, and as we mentioned, it’s not all about what happens in the ring.

The second largest spike we see above was the result of Floyd Mayweather, who hasn’t fought since September 2015, pleading with the UFC to allow a ‘superfight’ with Conor McGregor to go ahead. Neither Mayweather or McGregor have competed recently, nor have they any future fights scheduled, yet they still find themselves as the two most discussed individuals in this category. The bubble chart below showing the most mentioned topics from the boxing category further highlights this.

Most mentioned topics

Most shared on social media

Facebook

  1. Floyd Mayweather ‘officially out of retirement for Conor McGregor’ fight (FOX Sports. 56,951 shares)
  2. Bad refs, greedy NBF officials frustrating boxers – Apochi (Punchng. 42,367 shares)

Linkedin

  1. David Haye has Achilles surgery after Tony Bellew defeat (BBC. 234 shares)
  2. David Haye rules out retirement as he targets Tony Bellew rematch (BBC. 130 shares)

Reddit

  1. Teenage kickboxer dies after Leeds title fight (BBC. 1,502 shares)
  2. Muhammad Ali family vows to fight Trump’s ‘Muslim ban’ after airport detention (The Independent. 1,147 shares)

3. Stocks

The graph below shows publication volumes in the Stocks subcategory throughout the month of March 2017. In total we collected just over 30,000 articles.

Volume of stories published: Stocks

Top stories

The three stories that contributed to the biggest spikes in news publication volumes;

  1. Retailer Target sees stock drop by 13.5% after consumers boycott their pro-transgender stance.
  2. The US Federal Reserve increases interest rates, adding further pressure to housing market.
  3. Oil drops below US$53 as report shows rising US crude stockpiles

Most mentioned locations

Rather than focusing solely on extracted topics for this category, we thought it would be interesting to separate mentions of both locations and organizations. The chart above shows the most mentioned locations from all 30,000 articles published under the Stocks subcategory in March:

Most mentioned organizations

The chart above shows the top mentioned organizations including well known banks, investment firms and sources. It is interesting to see the likes of Facebook, Twitter and Snapchat in the mix also.

In March we saw Barclays declare Facebook as “the stock to town for the golden age of mobile”, referring to the upcoming 3-5 year period. Earlier in the month, Snapchat closed their first day of public trading up 44% at $24.48 a share.

Most shared on social media

Facebook

  1. Trump’s Approval Rating Hits New Record Low (Slate. 39,582 shares)
  2. Target Retailer Hits $15 Billion Loss Since Pro-Transgender Announcement (Breitbart. 30,107 shares)

Linkedin

  1. How on earth did India come up with these GDP numbers? (QZ. 2,579 shares)
  2. Home Prices in 20 U.S. Cities Rise at Fastest Pace Since 2014 (Bloomberg. 1,601 shares)

Reddit

  1. Bernie Sanders and Planned Parenthood are the most popular things in America, Fox News finds (The Week. 28,075 votes)
  2. GameStop Is Going to Close at Least 150 Stores (Fortune. 4,982 votes)

Conclusion

We hope that this post has given you an idea of the kind of in-depth and precise analyses that our News API users are performing to source and analyze specific news content that is of interest to them.

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





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Introduction

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.

With so many of our customers here at AYLIEN using our Text Analysis and News APIs to source and analyze alternative data in the form of unstructured content, we thought we would take a look at this trend to give you an idea of how and why it is becoming so popular and important.

What is alternative data?

Alternative data can be described as data that has been derived from non-traditional sources. Data that can be used to complement traditional data sources to produce improved analytical insights that would otherwise not have been achievable with traditional data alone.

Put simply, it’s data that isn’t commonly being used within a specific industry or use case, but can potentially be used to gain a competitive advantage over those that do not have access to it.

Let’s look at investors as an example. Ask any investor what data source they could not do without and they’ll most likely say it’s their Bloomberg terminal, or a similar device. Bloomberg’s data services enable investors to easily scan financial data that has been generated by thousands of companies. The ubiquity of terminals such as Bloomberg’s means that every investor requires one to be successful. However, it therefore also makes it difficult for investors to gain any sort of competitive advantage, seeing as they’re all receiving the same data, at the same time.

So, how is alternative data being sourced and utilized?

Alternative data in use

To give you an idea of just how significant alternative data can be, and the seemingly endless channels from which it can be sourced, we’re going to look at recent examples involving three brands; Chipotle, GoPro and JCPenney.

1. Chipotle

The CEO of Foursquare, which is a search-and-discovery service mobile app, predicted a 30% drop in Q1 sales for Chipotle, based on footfall data accumulated by their app users. Foursquare could see a drop in customer footfall based on a decrease in their users ‘checking-in’ at Chipotle restaurants, and they were correct.

Screenshot 2017-03-31 at 7.01.03 PMSource: https://medium.com/foursquare-direct/foursquare-predicts-chipotle-s-q1-sales-down-nearly-30-foot-traffic-reveals-the-start-of-a-mixed-78515b2389af#.bycy28hgd

2. GoPro

In November of last year, Wall Street was shocked by the news that GoPro had reported a loss of 60 cents per share on $240.56 million in revenue, after analysts had predicted a far less severe loss of 36 cents per share on $314.06 million in revenue.

While Wall Street never saw this coming, Quandl, a financial data provider were able to foresee the drop in GoPro earnings. In this case, the data came from electronic receipts extracted from over 3 million inboxes. A significant decline in email sales receipts from GoPro’s biggest distribution channel, Amazon, was a key indicator of what was to come. The graph below shows a clear drop in Q3 sales from Amazon, in contrast to other channels;

GoPro revenue estimates by channel

Screenshot 2017-03-31 at 7.02.37 PMSource: https://blog.quandl.com/email-receipts-predicted-gopros-q3-earnings

3. JCPenney

When JCPenney reported their Q2 results in 2015, the news came as a surprise to most investors. However, some investors were not surprised at all, because they had been tracking satellite imagery of JCPenney parking lots in near real-time which showed a clear trend in increasing customer footfall.

Screenshot 2017-03-31 at 7.04.18 PMSatellite images of JCPenney parking lots indicated an increase in customer footfall

These three examples show alternative data being sourced from a social app, email receipts and satellite imagery, highlighting the breadth and variety of potential sources that can be explored.

How can NLP and Text Analysis help?

These days we have more data at our fingertips than ever before. One of the main challenges is extracting meaningful insights from it, particularly from vast amounts of unstructured data. Advancements in Natural Language Processing (NLP), Machine Learning and Text Analysis are assisting analysts in exploring data sources that previously would not have been considered worthwhile to their use case or industry.

Here are a few examples:

Social media

Social media channels such as Twitter and Facebook are a gold mine of public opinion and information just waiting to be tapped. If you want to know how the public feels about a specific brand, individual or event, social media offers an easily accessible data source that can be analyzed for trends to help predict consumer behavior.

For example, our sentiment analysis of 1.7 million tweets during last year’s Super Bowl showed Amazon to be the most talk-about brand during the game. Their ad for the Echo also received the highest level of positive sentiment. Result: the Amazon Echo shot up to second place in the bestseller list within a week.

News stories

Highly advanced yet accessible NLP advancements have made it super easy to source and analyze large amounts of news content that can be narrowed down by countless search parameter combinations to deliver precise results to the end-user.

One relevant example we have been seeing among our News API users is the analysis of press releases containing mentions of specific keywords, individuals and companies that are of interest to, for example, an investor.

An investor may have a portfolio of multiple startup companies and may also be keeping a close eye on numerous others. By automatically pulling stories containing mentions of ‘startups’, companies within their own portfolio or companies in a similar area, as soon as they are published, they are accessing the very latest information and can therefore act accordingly and without delay.

Screenshot 2017-03-31 at 5.59.52 PMSearch result from our live News API demo

Some users have even automated their trading process based on press releases, earnings announcements and news of acquisitions.

Customer reviews

The analysis of online customer reviews at scale provides an opportunity for savvy sales professionals and marketers looking to spot a need or weakness in the product or service being reviewed. For example, we analyzed the sentiment of 500 hotel reviews and, using Aspect-Based Sentiment Analysis, we were able to uncover how reviewers felt about specific hotel aspects, such as location, beds, food, staff and WiFi.

As you can see from the charts below, where green = positive and red = negative, the hotel particularly fell down in the areas of beds, WiFi and overall value. As a seller of beds or WiFi solutions, for example, we would find this data extremely useful.

Screenshot 2017-03-31 at 7.12.40 PMAspect-Based Sentiment Analysis of hotel reviews

Conclusion

While we’ve merely scratched the surface today we hope we’ve given you a useful and insightful introduction to alternative data and how it is being used more and more to gain competitive advantage in a multitude of use cases and industries.





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