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

Introduction

Super Bowl 51 had us on the edge of our seats. A dramatic comeback and a shocking overtime finish meant the 111.3 Million Americans who tuned into the event certainly got what they came for. Even though TV viewership was down on previous years, the emotional rollercoaster that was Sunday’s game will certainly go down as one of the greatest.

As with any major sporting event, the Super Bowl creates an incredible amount of hype, particularly on Social Media. All of the social chatter and media coverage around the Super Bowl means it’s a fantastic case study in analyzing the voice of fans and their reactions to the event. Using advanced Machine Learning and Natural Language Processing techniques, such as Sentiment Analysis, we are able to understand how fans of both the Patriots and the Falcons collectively felt at any given moment throughout the event.

Not familiar with Sentiment Analysis? Sentiment Analysis is used to detect positive or negative polarity in text and can help you understand the split in opinion from almost any body of text, website or document.

Our process

We used the Twitter Streaming API to collect a total of around 2.2 million tweets that mentioned a selection of game and team-related keywords, hashtags and handles. Using the AYLIEN Text Analysis API, we analyzed each of these tweets and visualized our results using Tableau. In particular, we were interested in uncovering and investigating the following key areas:

  • Volume of tweets before, during and after the game
  • Sentiment of tweets before, during and after the game
  • Team-specific fan reactions
  • The most tweeted players
  • The most popular Super Bowl hashtag

Keyword selection

We focused our data collection on keywords, hashtags and handles that were related to Super Bowl 51 and the two competing teams, including;

#SB51, #superbowl, #superbowlLI, #superbowl51, #superbowl2017, #HouSuperBowl, #Patriots, #NEPatriots, #newenglandpatriots, #Falcons, #AtlantaFalcons.

Once we collected all of our tweets, we spent a bit of time cleaning and prepping our data set, first by disregarding some of the metadata which we felt we didn’t need. We kept key indicators like time stamps, tweet ID’s and the raw text of each tweet. We also removed retweets and tweets that contained links. From previous experience, we find that tweets containing links are mostly objective and generally don’t hold any author opinion towards the event.

Tools we used

Visualizations

Like with many of our data-driven blog posts, we used Tableau to visualize our results. All visualizations are interactive and you can hover your mouse over each one to dive deeper into the key data points from which they are generated.

We began our analysis of Super Bowl 51 by looking at the overall volume of tweets in the lead up and during the game.

Tweet volume over time: all tweets

The graph below represents minute-by-minute fluctuations in tweet volumes before during and after the game. For reference, we’ve highlighted some of the key moments throughout the event with the corresponding spikes in tweet volume.

As you can see, there is a definite and steady increase in tweet volume in the period leading up to the game. From kickoff, it is then all about reactions to in-game highlights, as seen by the sharp spikes and dips in volumes. We’ve also highlighted the halftime period to show you the effect that Lady Gaga’s performance had on tweet volumes.

Let’s now take a closer look at the pre-game period and in particular, fan predictions.

Pre-game tweet volume: #PatriotsWin vs. #FalconsWin

For the past 13 years, video game developers EA Sports have been using their football game ‘Madden NFL’ to simulate and predict the winner of the Super Bowl each year. They now have a 10-3 success-failure rate, in case you were wondering! In recent times, they have also been inviting the Twittersphere to show their support for their team by using a certain hashtag in their tweets. For 2017, it was #PatriotsWin vs. #FalconsWin.
So, which set of fans were the most vocal in the 2017 #MyMaddenPrediction battle? We listened to Twitter in the build up to the game for mentions of both hashtags, and here’s what we found;

58.57% of tweets mentioned #FalconsWin while 41.43% went with #PatriotsWin. While the Patriots were firm pre-game favorites, it is likely that the neutral football fan on Twitter got behind the underdog Falcons as they chased their first ever Super Bowl win, in just their second appearance.

Tweet volume over time by team

Now that we’ve seen the overall tweet volume and the pre-game #MyMaddenPrediction volumes, let’s take a look at tweet volumes for each individual team before, during and after the game.
The graph below represents tweet volumes for both teams, with the New England Patriots in the top section and the Atlanta Falcons in the bottom section.

Talk about a game of two halves! That vertical line you can see between the two main peaks represents halftime, and as you can see, Falcons fans were considerably louder in the first half of the game, before the Patriots fans brought the noise in the second half as their team pulled off one of the greatest comebacks in Super Bowl history.

Sentiment analysis of tweets

While tweet volumes relating to either team can be a clear indicator of their on-field dominance during various periods of the game, we like to go a step further and look at the sentiment of these tweets to develop an understanding of how public opinion develops and fluctuates.

The charts below are split into two sections;

Top: Volume of tweets over time, by sentiment (Positive / Negative)

Bottom: Average sentiment polarity over time (Positive / Negative)

New England Patriots

What’s immediately clear from the chart above is that, for the majority of the game, Patriots fans weren’t too happy and it seems had given up hope. However, as you can see by the gradual increase in positive tweets sentiment and volume in the final third, their mood clearly and understandably changes.

Atlanta Falcons

In stark contrast to the Patriots chart, Falcons fans were producing high volumes of positive sentiment for the majority of the game, until the Patriots comeback materialized, and their mood took a turn for the worse, as indicated by the drop of sentiment into negative.

Most tweeted individuals

To get an understanding of who people were talking about in their tweets, we looked at the top mentioned individuals. Unsurprisingly, Tom Brady was heavily featured after his 5th Super Bowl triumph.However, the most mentioned individual had no part to play in the actual game.

All notable players and scorers (and even Brady himself) were shrugged aside when it came to who the viewers were talking about and reacting to most on Twitter, as halftime show performer Lady Gaga dominated. To put the singer’s domination into perspective, she was mentioned in nearly as many tweets as Brady and Ryan were combined!

To get an idea of the scale of her halftime performance, check out this incredible timelapse;


Interestingly, national anthem singer Luke Bryan was tweeted more than both the Patriots’ Head Coach Bill Belichick and catch-of-the-game winner Julian Edelman. Further proof, if needed, that the Super Bowl is not just about the game of football, but that it is becoming more and more of an entertainment spectacle off the field.

Most popular Super Bowl hashtags

We saw a variety hashtags emerge for the Super Bowl this year, so we decided to see which were the most used. Here are the top 5 most popular Super Bowl hashtags, which we have visualized with volumes below;

#SuperBowl

#SB51

#SuperBowl2017

#SuperBowlLI

#SuperBowl51

Despite the NFL’s best efforts to get Twitter using #SB51, the most obvious and simple hashtag of #SuperBowl was a clear winner.

Conclusion

There is no other event on the planet that creates as much hype in the sporting, advertising and entertainment worlds. But the Super Bowl as we know it today, is far less about the football and more about the entertainment factor and commercial opportunity. With big brands spending a minimum $5 Million for a 30 second commercial, competition for viewers eyes and more importantly viewers promotion through shares and likes on social media, the Super Bowl has become big business.

In our next installment, we’ve analyzed the chatter around Super Bowl 51 from a branding point of view. We collected and analyzed Twitter data and news and media coverage of the event to pinpoint which brands and commercials joined the Patriots as Super Bowl 51 champions.





Text Analysis API - Sign up




0

Introduction

It’s certainly an exciting time be involved in Natural Language Processing (NLP), not only for those of us who are involved in the development and cutting-edge research that is powering its growth, but also for the multitude of organizations and innovators out there who are finding more and more ways to take advantage of it to gain a competitive edge within their respective industries.

With the global NLP market expected to grow to a value of $16 billion by 2021, it’s no surprise to see the tech giants of the world investing heavily and competing for a piece of the pie. More than 30 private companies working to advance artificial intelligence technologies have been acquired in the last 5 years by corporate giants competing in the space, including Google, Yahoo, Intel, Apple and Salesforce. [1]

It’s not all about the big boys, however, as NLP, text analysis and text mining technologies are becoming more and more accessible to smaller organizations, innovative startups and even hobbyist programmers.

NLP is helping organizations make sense of vast amounts of unstructured data, at scale, giving them a level of insight and analysis that they could have only dreamed about even just a couple of years ago.

Today we’re going to take a look at 3 industries on the cusp of disruption through the adoption of AI and NLP technologies;

  1. The legal industry
  2. The insurance industry
  3. Customer service

NLP & Text Analysis in the Legal industry

While we’re still a long long way away from robot lawyers, the current organic crop of legal professionals are already taking advantage of NLP, text mining and text analysis techniques and technologies to help them make better-informed decisions, in quicker time, by discovering key insights that can often be buried in large volumes of data, or that may seem irrelevant until analyzed at scale, uncovering strategy-boosting and often case-changing trends.

Let’s take a look at two examples of how legal pro’s are leveraging NLP and text analysis technologies to their advantage;

  • Information retrieval in ediscovery
  • Contract management
  • Article summarization

Information retrieval in ediscovery

Ediscovery refers to discovery in legal proceedings such as litigation, government investigations, or Freedom of Information Act requests, where the information sought is in electronic format. Electronic documents are often accompanied by metadata that is not found on paper documents, such as the date and time the document was written, shared, etc. This level of minute detail can be crucial in legal proceedings.

As far as NLP is concerned, ediscovery is mainly about information retrieval, aiding legal teams in their search for relevant and useful documents.

In many cases, the amount of data requiring analysis can exceed 100GB, when often only 5% – 10% of it is actually relevant. With outside service bureaus charging $1,000 per GB to filter and reduce this volume, you can start to see how costs can quickly soar.

Data can be filtered and separated by extracting mentions of specific entities (people, places, currency amounts, etc), including/excluding specific timeframes and in the case of email threads, only include mails that contain mentions of the company, person or defendant in question.

Contract management

NLP enables contract management departments to extract key information, such as currency amounts and dates, to generate reports that summarize terms across contracts, allowing for comparisons among terms for risk assessment purposes, budgeting and planning.

In cases relating to Intellectual Property disputes, attorneys are using NLP and text mining techniques to extract key information from sources such as patents and public court records to help give them an edge with their case.

Article summarization

Legal documents can be notoriously long and tedious to read through in their entirety. Sometimes all that is required is a concise summary of the overall text to help gain an understanding of its content. Summarization of such documents is possible with NLP, where a defined number of sentences are selected from the main body of text to create, for example, a summary of the top 5 sentences that best reflect the content of the document as a whole.

NLP & Text Analysis in the Insurance industry

Insurance providers gather massive amounts of data each day from a variety of channels, such as their website, live chat, email, social networks, agents and customer care reps. Not only is this data coming in from multiple channels, it also relates to a wide variety of issues, such as claims, complaints, policies, health reports, incident reports, customer and potential customer interactions on social media, email, live chat, phone… the list goes on and on.
The biggest issue plaguing the insurance industry is fraud. Let’s take a look at how NLP, data mining and text analysis techniques can help insurance providers tackle these key issues;

  • Streamline the flow of data to the correct departments/agents
  • Improve agent decision making by putting timely and accurate data in front of them
  • Improve SLA response times and overall customer experience
  • Assist in the detection of fraudulent claims and activity

Streamlining the flow of data

That barrage of data and information that insurance companies are being hit by each and every day needs to be intricately managed, stored, analyzed and acted upon in a timely manner. A missed email or note may not only result in poor service and an upset customer, it could potentially cost the company financially if, for example, relevant evidence in a dispute or claim case fails to surface or reach the right person/department on time.

Natural Language Processing is helping insurance providers ensure the right data reaches the right set of eyeballs at the right time through automated grouping and routing of queries and documents. This goes beyond simple keyword-matching with text analysis techniques used to ‘understand’ the context and category of a piece of text and classify it accordingly.

Fraud detection

According to a recent report by Insurance Europe, detected and undetected fraudulent claims are estimated to represent 10% of all claims expenditure in Europe. Of note here, of course, is the fraud that goes undetected.

Insurance companies are using NLP and text analysis techniques to mine the data contained within unstructured sources such as applications, claims forms and adjuster notes to unearth certain red flags in submitted claims. For example, a regular indicator of organized fraudulent activity is the appearance of common phrases or descriptions of incidents from multiple claimants. The trained human eye may or may not be able to spot such instances but regardless, it would be a time consuming exercise and likely prone to subjectivity and inconsistency from the handler.

The solution for insurance providers is to develop NLP-powered analytical dashboards that support quick decision making, highlight potential fraudulent activity and therefore enable their investigators to prioritise cases based on specifically defined KPIs.

NLP, Text Analysis & Customer Service

In a world that is increasingly focused on SLAs, KPIs and ROIs, the role of Customer Support and Customer Success, particularly in technology companies, has never been more important to the overall performance of an organization. With the ever-increasing number of startups and innovative companies disrupting pretty much every industry out there, customer experience has become a key differentiator in markets flooded with consumer choice.

Let’s take a look at three ways that NLP and text analysis is helping to improve CX in particular;

  • Chat bots
  • Analyzing customer/agent interactions
  • Sentiment analysis
  • Automated routing of customer queries

Chat bots

It’s safe to say that chat bots are a pretty big deal right now! These conversational agents are beginning to pop up everywhere as companies look to take advantage of the cutting edge AI that power them.

Chances are that you interact with multiple artificial agents on a daily basis, perhaps even without realizing it. They are making recommendations as we online shop, answering our support queries in live chats, generating personalized fitness routines and communicating with us as virtual assistants to schedule meetings.

Screen Shot 2016-09-16 at 12.21.48

A recent interaction I had with a personal assistant bot, Amy
Chat bots are helping to bring a personalized experience to users. When done right, not only can this reduce spend in an organization , as they require less input from human agents, but it can also add significant value to the customer experience with intelligent, targeted and round-the-clock assistance at hand.

Analyzing customer/agent interactions

Interactions between support agents and customers can uncover interesting and actionable insights and trends. Many interactions are in text format by default (email, live chat, feedback forms) while voice-to-text technology can be used to convert phone conversations to text so they can be analyzed.

Listening to their customers

The voice of the customer is more important today than ever before. Social media channels offer a gold mine of publicly available consumer opinion just waiting to be tapped. NLP and text analysis enables you to analyze huge volumes of social chatter to help you understand how people feel about specific events, products, brands, companies, and so on.

Analyzing the sentiment towards your brand, for example, can help you decrease churn and improve customer support by uncovering and proactively working on improving negative trends. It can help show you what you are doing wrong before too much damage has been done, but also quickly show you what you are doing right and should therefore continue doing.

Customer feedback containing significantly high levels of negative sentiment can be relayed to Product and Development teams to help them focus their time and efforts more accordingly.

Because of the multi-channel nature of customer support, you tend to have customer queries and requests coming in from a variety of sources – email, social media, feedback forms, live chat. Speed of response is a key performance metric for many organizations and so routing customer queries to the relevant department, in as few steps as possible, can be crucial.

NLP is being used to automatically route and categorize customer queries, without any human interaction. As mentioned earlier, this goes beyond simple keyword-matching with text analysis techniques being used to ‘understand’ the context and category of a piece of text and classify it accordingly.

Conclusion

As the sheer amount of unstructured data out there grows and grows, so too does the need to gather, analyze and make sense of it. Regardless of the industry in which they operate, organizations that focus on benefitting from NLP and text analysis will no doubt gain a competitive advantage as they battle for market share.

 


Text Analysis API - Sign up




0