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One of the major challenges with mining the Web and Social Media for insights is trying to get all of your data into one place. To do this, you need to extract information from multiple sources in order to gain an accurate and holistic view.

Combining multiple data sources and analyzing their content can be a daunting task, but thankfully data mining frameworks such as RapidMiner and Weka make it easy to extract information from multiple sources in a quick and straightforward manner.

In this blog post, we’re going to show you how to use AYLIEN’s Text Analysis API from within RapidMiner to analyze text gathered from sources on the web.

The Web Mining extension for RapidMiner provides access to internet sources like web pages, RSS feeds, and web services. In this tutorial, we’re going to use it to make HTTP requests to the Text Analysis API. In part 2 we will use it to scrape information from web pages such as Rotten Tomatoes.

Requirements

  • RapidMiner v5.3+ (download)
  • Text Analysis API key (subscribe for free here)

Step 1: Install Web Mining for RapidMiner

  • Open the RapidMiner Marketplace by selecting Help > Updates and Extensions (Marketplace)
  • Search the Marketplace for “Web Mining” and install the extension

Step 2: Setup the API call

The Web Mining package provides you with an operator for invoking external web services. This operator is called “Enrich Data by Webservice” and can be found in the Operators panel under Web Mining > Services > Enrich Data by Webservice.

  • Drag and drop an instance of the Webservice operator into your process
  • Select the operator to access its configuration parameters
  • Set the following values for the parameters:
    • url: “https://api.aylien.com/api/v1/sentiment?mode=tweet&text=<%text%>” or if you’re using Mashape: “https://aylien-text.p.mashape.com/sentiment?mode=tweet&text=<%text%>”
    • request method: POST
    • body: “text=<%title%>”
    • request properties:
      • Accept: “text/xml”
      • X-AYLIEN-TextAPI-Application-Key: “YOUR_API_KEY”
      • X-AYLIEN-TextAPI-Application-ID: “YOUR_APPLICATION_ID”
      • If you’re using Mashape: X-Mashape-Key: “YOUR_API_KEY”
    • query type: XPath
    • xpath queries:
      • polarity: “//polarity/text()”

Here we are basically calling the /sentiment endpoint of the Text Analysis API to analyze the sentiment of some text in order to find out if it’s positive, negative or neutral.

Step 3: Setup the input text

Now that our API call is setup, we need to provide the operator with some input text.

  • Install the Text Processing extension, the same way you installed the Web Mining extension in Step 1
  • Add an instance of the Text Processing > Create Document operator
  • Select the Create Document operator and add some text by clicking Edit Text
  • Add the Text Processing > Documents to Data operator to convert the Document to an ExampleSet, and set the text attribute parameter to “text”
  • Add the Web Mining > Utility > Encode URLs operator to URL-encode the text, and set the url attribute parameter to “text”
  • Finally, connect the URL-encoded text input to the Enrich Data by Webservice operator created in Step 2

Step 4: Run!

Now that we have everything setup, it’s time to run our process by clicking the Run button.

As you can see, “I love puppies!” was deemed to be positive and the result is now accessible in RapidMiner for further analysis and reporting. You could use one of the many other methods provided in the Text Processing package to generate any number of documents and analyze their sentiment in the same fashion. Also, by changing the url parameter in the API call you can access any other endpoint from the Text API (Concept Extraction, Classification, Summarization and so on).

Next stop: analyzing movie reviews

In the 2nd part of this series, we’re going to crawl Rotten Tomatoes with RapidMiner to extract movie reviews and analyze their sentiment to gain some interesting insights.

For more examples of how we used a similar setup to analyze millions of tweets about various events such as World Cup 2014 and the #AppleLive event, check out our previous blog posts.

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The internet has had a massive impact on marketing and advertising in general. It has provided an effective way for businesses to access target prospects with branded and targeted marketing material at scale. However, how effective are traditional digital advertising techniques? Have we become immune to flashy banner ads and keyword focused promotional material? Apparently not! But it seems things can improve.

Online Advertising

Spending on ads served to internet enabled devices, desktops, laptops, mobiles and tablets will reach $137.53 billion this year and will continue to grow, according to eMarketer’s latest estimates of worldwide paid media spending.

Advertising online is based around matching ads or promotional material (banner ads, links, video and interactive ads) with appropriate web pages where the right audience will see them. Traditionally ad targeting is done by manual classification of pages or by using information retrieval techniques to find keywords from the page, and match these to keywords associated with ads.

While this has proven to be a pretty effective promotion channel thus far, it does have its problems. It is true that a lot about the effectiveness of an ad is down to the creative, the look and feel, the text used etc but if it’s showing up in the wrong place in front of the wrong people it isn’t going to be effective.

Relevance is key!

Ads today are often intrusive, robotic and just not relevant! Well placed and effective ads all have particular attributes that stand out from the rest. They are relevant and they promote a product or service that the visitor is likely to be interested in.

In the case below I visited a few pages to see how they faired by way of “targeted” advertising. The ad served to me on Mashable was for Eukanba dog food. Is this relevant?  I don’t have a dog and I have never bought or researched dog food online. It also isn’t relative to anything else on the page and therefore there is very little chance I would click on that ad.

 

poorly targeted advertising

 

So what can we do to get more effective ads in front of the right people? By incorporating text analysis and semantic capabilities into ad placement strategies, we can focus on more than just keyword matching and serve relevant ads in the right place at the right time.

What is semantic targeting?

Semantic advertising aims to analyze web pages to properly understand and classify the meaning of the page in order to ensure that viewers of the page are shown the most appropriate ads.

Semantically targeted ads increase the chance that the viewer will “click-through” because only ads that are relevant to what the user is viewing or the page they are on will be displayed. For example, say you visit a mountain biking blog, you are far more likely to click on an ad for cycling gear or bike helmets than one for car insurance as it is far more relevant at that time.

Advantages of Semantic Ads

Focused on more than keywords

Words can have multiple meanings and scanning web pages for keywords in order to serve certain ads isn’t always effective. For example, the word “apple” may result in ads being displayed about Apple accessories or an organic fruit delivery service which means depending on the meaning of the word relevant to the content it’s included in the ad could be very poorly or well targeted. A better approach would be to analyze the rest of the page to understand the context and if there are mentions of other fruits and organic farming etc… It is probably safe to say the delivery service as would be more appropriate.

Brand Protection

Ads can often turn up in some pretty inappropriate places if they are targeted by one factor and one factor only, say keywords for example.

 

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In the example above, the ad was most likely targeted to the webpage visitor based on keyword matching. Matching “grilling” as a keyword in the article title with the grilling competition advertisement. Is this ad relevant? No. Does it promote the company’s brand in a positive light? No. Is it an effective advertisement? Certainly not.

Not behaviour based

These days we have become a lot more private in our web use. People are conscious of behaviours being tracked for advertising purposes whether for display ads or retargeting techniques and have become a lot more savvy by choosing to block ads completely where possible, using private search engines or by clearing their cookies on a regular basis. This poses a significant problem as it takes away a particularly effective form of targeting based on behavioural tracking. Semantic ad Targeting allows advertisers to move away from behaviour tracking and think on their feet by serving relevant ads in real time based on analysis of the text on pages.

Conclusion

Using NLP and Text Analysis techniques advertisers can analyze web pages to understand the context of keywords, extract entities and concepts mentioned in a text and classify webpages automatically. Allowing them to look beyond keywords and search terms to automatically match ads with relevant content on webpages. Being smarter and more strategic about how we target prospects and embracing new technology, could one day mean that ads will become so relevant that, we actually find them useful and don’t feel the need to block them.





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Customer segmentation and persona development is a key step in the development of a marketing strategy. It involves the grouping together of customers and potential prospects within a marketplace based on their similarities. Customers can be segmented in any number of ways including by geographic location, customer profitability, perceived benefit from a given product feature, preferred communication channel and so on. The key is to segment your customers in a way that best delivers on business goals.

Traditionally marketers would focus on high-level generic similarities. For example, a Craft brewer may be targeting a customer “segment” represented by males, 17-35, based in the UK. Going a level deeper, we can look to create customer personas. A persona is literally a personification of the characteristics of the customer segment. In the case of the brewery their Persona might be “Bob” who is male, aged between 17 and 35, enjoys watching and playing sports, loves good food, has a beard and is partial to the occasional beer.

Identifying Customer Personas using Text Analysis

We share a lot of what businesses would class as “useful” information on the web today. Our contact details, what brands we like, what bands we listen to, where our last holiday was, where we ate lunch…we pretty much share everything and mainly through social channels.

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With the help of Text Analysis, a lot of this information can be mined to create incredibly targeted Customer Personas, by creating a database of your customers’ “taste” profiles. “Taste” profiles consist of a list of what customers like and dislike with a weighting score to indicate how much they like or dislike something. Taking the database as a whole we can look for similarities to uncover personas and use these insights to tailor our messaging to be more effective.

Sample Taste Graph:

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Using Text Analytics and data mining, we can analyse online interactions like tweets, reviews on yelp, comments and likes and shares. Modern technology and solutions allow us to dive into what our personas like about a product, a news article, what their interests are, where they ate lunch and even if they enjoyed it!

Once you have nailed your target personas you can utilise the same social media platforms to identify new customers, who fit your target persona and nurture existing ones with super targeted marketing communications in the right channels that are sure to resonate.

How to leverage your personas 
Individuals like celebrities or social influencers have a greater voice on social media, but not all us can afford a celebrity endorsed tweet.

Social media influencers however are a little easier to access. They are generally passionate and knowledgeable in their area of interest and are regarded as trustworthy authorities by their followers who will be open and receptive to what they have to share.

While it may seem difficult to do, identifying influencers can actually be accomplished quite easily with the right tools and technology. On social platforms, we can define keywords to monitor, classify text they share in comments, content or posts, extract entities and concepts and use Sentiment Analysis techniques to discover the polarity of aspects in the text.

This data can then be filtered to uncover the most relevant influencers for a given segment, those with the largest and most engaged audiences based on the number of shares, likes, upvotes, retweets, followers etc…

In summary, Text Analysis allows you to listen to the voice of your customers and prospects giving you a better understanding of their interests and allowing you to get more targeted in identifying target personas or finding social influencers.





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The publishing industry has changed dramatically. Mainstream newspapers and magazines have given way to desktop publishing and the Internet as economics have changed the game.

Let’s look at the main drivers behind this change.

More competition – Self-publishing has moved into mainstream online channels. The increase of entrants into the market means more choice and much of it is free.

The introduction of Apps – Apps create a more engaging and effective way to interact with an audience. The ever increasing ownership and usage of mobile devices mean that more readers can be reached.

Real-time social sharing – It can be argued that Facebook and Twitter provide the most up-to-date news channels. The sharing dimension can also be very appealing to readers who want to contribute to reporting the news, as opposed to passively receiving it.

Shift from mass to a niche market – Before the inception of the internet, successful newspapers and magazines appealed to the general public. Today, however, digital publishing has far lower production costs and a far greater reach to service niche markets.

 

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According to Ofcom, use of the internet to consume news has increased for computers, laptops, tablets and mobiles since 2013, while TV has seen a small decrease from 78 to 75 percent. Use of any type of an online platform to consume news increased from 32 to 41 percent this year, and is now higher than the use of newspapers (40 percent) and radio (36 percent).

This shift in how we consume news has forced publishers to change their strategies in order to compete. More specifically, publishers understand that their content needs to be more relevant, richer, interactive, timely and discoverable.

An Example

Let’s say an editor hears about a bus crashing near a major school, close to a fire station. The editor wants to write about the story and they want to include historical information about the cause of bus crashes (e.g. time of day, time of year, equipment malfunction, driver error etc based on other bus crashes for the past 30 years) to give the story more depth and context. In most cases, a journalist would have tagged documents with dates and keywords. This is generally a manual process and therefore documents could easily be left untagged due to human error. Tags may be missed if different individuals are involved in the process. Some people may also not be as thorough as others. For instance, if somebody simply tags the document “bus crash”, it might be very difficult to find similar stories, much less analyze what happened in other relevant crashes.

Enter Text Analytics

By incorporating text analysis software, historical data can be culled for relevant concepts, entities, sentiments and relationships to produce a far richer tagging system. Information about the bus crash such as the type of bus involved, location, times, dates and causes could be extracted from the text. These entities would be kept as metadata about the articles and used when needed.

The Benefits

Text Analysis software can ‘understand’ the relationships between articles and provide suggestions to similar content. This benefits the editor as he or she can be far more productive as they navigate easily through a complete dataset. Research is, therefore, easier, a lot of time is saved and the end product is often richer, as the editor can reference similar events and give more depth and context to their article.

Richer, more relevant content can improve user engagement, meaning more page views by a narrower market, which can increase the potential for generating advertising revenue. A consumer that is more engaged with their content is far more likely to subscribe to niche newsletters, which can allow publishers to develop these relationships further and upsell their service to their consumers.

Our conclusion

Text analytics is essential in the publishing industry because it saves time when gathering data, allows you to produce richer content to attract more readers in narrower markets, where consumers are often more loyal.





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Apple Live probably got off to the worst start possible on Wednesday. Most of us who tried to log on to watch the much-anticipated launch were first, forced to watch the live feed in Safari and second, greeted with the TV Truck Schedule Screen…

To add to this Apple also made a complete mess of the audio. We were left sitting refreshing the page, waiting for the stream to start while being subjected to an audio visual nightmare, described brilliantly by this “fan” below:


Analyzing Tweets

At AYLIEN, we gathered 11 million+ tweets mentioning ‘Apple’, ‘iPhone’, ‘iOS’, ‘iPad’, ‘Mac’, ‘iPod’, ‘Macbook’, ‘iCloud’, ‘OS X’, ‘iWatch’ and ’#AppleLive’ from the 4th of September to the 10th of September with a view of analyzing the tweets to gain insight into the voice of Apple Followers.

The Tweets over time graph covers tweets from 4pm on launch day to 12am that night. The top half of the graph displays the volume of tweets by journalists and the bottom displays tweets by the general public.

What’s interesting here is the difference in what the two groups got excited and most vocal about. The general public was most vocal on Twitter in anticipation of the iPhone updates, not the Keynote, not the Apple Watch, not Apple Pay or not even one of the biggest rock bands in the world announcing a free album giveaway to over 500 million people, no, just an announcement to say the iPhone is going to be bigger! The journalists however, as a group were far less biased towards any one announcement and tweeted about what they thought were the biggest announcements of the day and not surprisingly, given recent hype in the tech industry around payments, they were most vocal about Apple Pay.

Tweet Polarity

The disappointment of #AppleLive was evident on Twitter especially at the start of the launch.

Running Sentiment Analysis on the tweets we can see from the graph above that the overall polarity of tweets took a sharp dive into the negative in the build up to the event. The public weren’t afraid of expressing their opinion as to how frustrated they were to be missing out on the action and Apple’s Audiovisual team must have been scrambling to make things right.

The graph above shows the polarity of tweets throughout the launch. It’s pretty clear the difficulty people had streaming the live cast put a dampener on the mood overall with the sharp switch from positive to negative around 5 pm. However, Apple did manage to turn things round with some well received announcements which brought them back into the green. While it’s interesting to see the overall polarity of the tweets, things start to get more interesting when we look at what aspects in particular about the event were p*ssing people off?

Aspect-Level Sentiment Analysis

Whether you are analyzing Tweets, Articles or Reviews the overall sentiment and knowing whether it is positive, negative or neutral is cool and useful to know. However, Sentiment Analysis gets a lot more valuable and interesting when we can identify what aspects of entities in particular are positive or negative. Aspect Level Sentiment Analysis refers to identifying opinions or sentiments expressed on different features or aspects of entities; a phone, a camera, a bank. The aspects or features of entities would be say, the screen of a phone or the battery of a watch.

Consider the following tweet as an example: “The iPhone 5s is amazing, I love the camera and the OS is so fast. The battery is terrible though.”

The entity here is the iPhone and the aspects are the Camera, OS and Battery. If you were to focus on tweet-level sentiment for this particular example it would most likely be tagged as a positive tweet and you would most probably miss out on the negativity related to the battery.

The word cloud below highlights what in particular were the negative aspects of the Tweets about the launch. This information would be important to Apple in analyzing their brand, the overall success of the event and to get an understanding of what in particular people liked and disliked about the event. The same could also be done for particular products that were announced.

 

Extracting Insight

For the purpose of this blog we decided to focus on one aspect of the launch in particular from the Apple Live event: the Apple Watch.

The word clouds relating to the Apple watch highlight some key points which give a strong insight into the public reaction to the Apple Watch.

 

Positive:

  • People “want” one!
  • They think it “looks” “pretty”
  • They like that it comes in “34 styles”
  • They are “excited” by it.

Negative:

  • Some think it looks “ugly”
  • They are wary of the “battery life”

In saying that it’s also pretty interesting that both the positive and negative tweets tell us that most people just care about how it looks over any of its fancy features!

Conclusion

Sentiment Analysis doesn’t have to stop at high level understanding of whether or not a sample of tweets, reviews, emails, NPS scores etc are positive or negative. The ability to dive deeper into the “what” and “why” of positive or negative sentiment allows us to gain a better understanding of public opinion.

Whether we are looking for indications of what features people like and dislike in a product or offering or what aspects they place most importance on we can gain a deeper insight into opinions by focusing on aspects in Sentiment Analysis.

 





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Text analysis newbie? Seasoned data junkie? Do you spend time classifying and analyzing text documents? Are you trying to listen to the voice of your customers online?

We are excited to launch a super cool and powerful tool that brings Text Analysis capabilities to Google Sheets.

With AYLIEN Text Analysis for Google Sheets you can:

  • Perform Sentiment Analysis on Social Media streams to identify what is positive, negative or neutral about your company or brand
  • Extract mentions of entities and concepts such as people, organizations, places and products from documents
  • Summarize long chunks of text and articles into a few key sentences
  • Detect the language of a document
  • Find the best hashtags for your content for better discoverability on Social Media
  • Classify your documents or links into more than 500 categories
  • Extract the full text of an article, as well as its author name, embedded media, etc.

We are really excited about this announcement as it brings the power of Text Analysis to data junkies, marketers and analysts who may not have the programming or technical expertise to use an API. This easy to use tool plugs straight into your spreadsheet and provides valuable insight from text in minutes.

The AYLIEN Text Analysis add-on is the first and only text analysis tool available for Google Sheets. It is the easiest way to perform sophisticated text analysis on documents without leaving your Google Sheets environment. Powered by the AYLIEN Text Analysis API, this tool brings an easy to use package of Natural Language Processing and Machine Learning tools to your spreadsheets. It allows you to transform your spreadsheet into a powerful yet easy-to-use Text Analysis tools with no coding or text analytics experience needed to get started.

Getting started is easy, check out our videos and walkthrough guides to see how easy it is to get up and running on our tutorial page. Or you can watch our intro video below.

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The AYLIEN Text Analysis Add-on is free to use for up to 1000 credits. Essentially 1 call = 1 credit. For users utilising more that 1000 credits credits can be topped up easily in the Chrome add-on store and your credits won’t expire.

Download your add-on today and start extracting reality from your data in your spreadsheets.






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We are happy to announce that as part of our strategic partnership with Telerik our Text Analysis API is now even easier for mobile developers to incorporate into their apps and development process.

Telerik Platform

Telerik provides a platform on which developers can build Mobile apps quickly and efficiently. The platform allows you to design, build, connect, test, deploy, manage and measure sophisticated mobile apps across multiple platforms including android, iOS and windows. The advantages of the platform mean it is easier for developers to add features and capabilities to native apps that would otherwise entail some heavy lifting and domain expertise.

Text Analysis and Mobile Apps

There is no denying mobile device usage has been on the rise in recent years and according to a study carried out by Flurry more and more of us are spending our browsing time in native apps and moving away from mobile web browsing. As much as 86% of web use on mobile devices is spent in native apps today. This has meant mobile development has risen high in priority as companies scramble to kick their mobile projects into gear.

What can be accomplished today with Text Analytics and the insight it can provide to developers and business stakeholders alike, has meant that there has also been a steady increase in adoption and application of Text Analysis practices in mobile app development.

Providing a quick and efficient way for mobile developers to harness and utilize the powers of Text Analysis in their apps and development process is something we have been focusing on in the past couple of months.

Our Partnership

Telerik recently announced the launch of an industry first verified plugin marketplace. This Cordova/PhoneGap verified marketplace aims to showcase and promote custom plugins which allow developers to extend the functionality of their mobile apps.

This Plugin marketplace provided an interesting opportunity for us at AYLIEN to bring the power of Text Analysis straight to the development process of mobile apps. The plugin allows sophisticated text analysis capabilities to be easily incorporated into a mobile app without any text analytics knowledge or expertise.

 

 

Our AYLIEN Plug-in is now available in the Telerik Plugin Marketplace. This plugin allows you to extract structured information and insights from text. It performs Summarization, Sentiment Analysis and Hashtag Suggestion for any piece of text (tweets, articles, form submissions, documents etc.).

To get started with the plugin:

Step 1. Grab your free AYLIEN Text Analysis API Key here.

Step 2. Download your plugin from the plugin marketplace here

Or alternatively, you can utilise the prebuilt demo app available in the Telerik Plug-in Marketplace to test it out first.

 

Learn more about our API Drop us an email @mention us on Twitter





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In the first part of our World Cup 2014 blog series, we analyzed 30 million tweets collected between June 6th and July 14th about the biggest sporting event in the world, FIFA World Cup 2014, and we looked at some high-level associations and insights about the tournament: in a nutshell, we observed a repeating pattern of spikes appearing in tweet volumes around match times and important events. In the second part of the series, we’re going to dive deep into the tweets and analyze their content using our very own Text Analysis API and Rapidminer to get a more in-depth view of the data.

Data

We’re using the same datasets that were used in part 1 (tweets.csv) plus a new dataset called tweets-sentiment.csv, which contains the sentiment polarity and subjectivity results obtained using our Sentiment Analysis API in tweet mode.

Top hashtags and mentions

Let’s start our analysis by finding the most popular hashtags and @ mentions from the tournament, by tokenizing tweets and sorting the tokens by frequency:

Sentiment Analysis

We’re now going to look into the polarity values (“positive” or “negative”) of these tweets to see what these values are for different entities and how they change over time, as a result of various events.

Note: we are only analyzing English tweets for the following examples, which introduces a sampling bias. The following charts and insights are based on the opinions of the English-speaking Twitter users.

Sentiment over time

Different events concerning players or a teams affect how people think and talk about them. Using polarity analysis, we can get an idea of people’s reaction to various events, which can provide valuable insight. Let’s look at two major talking points from the tournament as examples: Luis Suarez and the Brazil’s shocking performance.

1. On June 24th, Argentine Luis Suarez was largely accused of biting Italy defender Giorgio Chiellini, which was followed by a big wave of negative comments and feedback from Social Media. Suarez issued an apology on June 30th, which seems to have been satisfactory for the Twitter community (take note PR people!):

2. Brazil had arguably one of its worst performances in World Cup history during the 2014 tournament. This is pretty evident when you analyze the sentiment of tweets about #BRA after every lost match or controversial win:


Before the 3rd place playoff game between Brazil and Netherlands, people were hopeful that the catastrophic loss against Germany might bring the best out of the Brazilians. However, a few minutes into the game it’s pretty clear this was no longer the case:

Popularity by sentiment

We can use the average polarity measures for various entities to see how positively or negatively people talk about them.

Teams

Average polarity for the 16 teams that qualified for the second round:

Players

Average polarity for top 10 scorers as well as two noteworthy players, Tim Howard of USA and Luis Suarez of Argentina:

Most ‘polar’ hashtags and mentions

Finally, let’s look at some of the most positive and negative hashtags and mentions:

Final notes

Analyzing the sentiment of tweets gives an extraordinary view into the opinions of the public in relation to a certain topic or event. Listening to “social chatter” allows you to extract detailed insight into opinions and trends on brand, companies, events, football teams etc. and how they change over time, with say, the launch of a product, a company announcement, a crisis event or in the case above a footballer biting another player.

In Suarez’s case his “brand” took a major hit and “social chatter” about him turned pretty sour following the biting incident, however, his PR teams involvement and his deal at Barcelona allowed him to bounce back quite quickly, shown quite clearlyin the switch in polarity of tweets about him.

To learn more about Sentiment Analysis check out our recent blog posts. If you are Interested in analyzing the sentiment of text, tweets, comments or reviews you can get free access to our Text Analysis API.

 

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August 2014 will forever be a significant month in the lifetime of AYLIEN. We’ve doubled our team, upgraded our home and pimped our office swag – see new shiny “AYLIEN” tees!

The good people of Regus House on Harcourt Street gave us shelter for little over a year when we first came to Dublin and now we’ve decided to flee the serviced office nest to fend for ourselves.

Our new home is the 4th floor in Equity House, on Ormond Quay, Dublin 7. We’re delighted with our new space. We have a wrap around balcony overlooking the Liffey and the Four Courts, loads of natural light thanks to our huge Georgian windows, and our porter ‘Jerry’ is a bit of an auld legend.

 

 

The coolest thing about our new space is the size of it. This is of paramount importance to us. We’ve 3 new team members on board and plenty of room to hire more.

Our first weekend as “AYLIEN of Equity House” was spent in our new den, giving it some TLC. Our office space is awesome but we wanted it to become “AYLIEN awesome”. So paint brushes in hand and 5 litres of green Dulux at the ready, we got to work.

 

 

Fast forward 48 hours and 2 coats of paint and we have ourselves an “AYLIEN awesome” office.





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