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Intro

In recent months, we have been bolstering our sentiment analysis capabilities, thanks to some fantastic research and work from our team of scientists and engineers.

Today we’re delighted to introduce you to our latest feature, Sentence-Level Sentiment Analysis.

New to Sentiment Analysis? No problem. Let’s quickly get you up to speed;

What is Sentiment Analysis?

Sentiment Analysis is used to detect positive or negative polarity in text. Also known as opinion mining, sentiment analysis is a feature of text analysis and natural language processing (NLP) research that is increasingly growing in popularity as a multitude of use-cases emerge. Here’s a few examples of questions that sentiment analysis can help answer in various industries;

  • Brands – are people speaking positively or negatively when they mention my brand on social media?
  • Hospitality – what percentage of online reviews for my hotel/restaurant are positive/negative?
  • Finance – are there negative trends developing around my investments, partners or clients?
  • Politics – which candidate is receiving more positive media coverage in the past week?

We could go on and on with an endless list of examples but we’re sure you get the gist of it. Sentiment Analysis can help you understand the split in opinion from almost any body of text, website or document – an ideal way to uncover the true voice of the customer.

Types of Sentiment Analysis

Depending on your specific use-case and needs, we offer a range of sentiment analysis options;

Document Level Sentiment Analysis

Document level sentiment analysis looks at and analyzes a piece of text as a whole, providing an overall sentiment polarity for a body of text.

For example, this camera review;

Screen Shot 2016-11-22 at 17.56.07

receives the following result;

Screen Shot 2016-11-22 at 17.56.14

Want to test your own text or URLs? Check out our live demo.

Aspect-Based Sentiment Analysis (ABSA)

ABSA starts by locating sentences that relate to industry-specific aspects and then analyzes sentiment towards each individual aspect. For example, a hotel review may touch on comfort, staff, food, location, etc. ABSA can be used to uncover sentiment polarity for each aspect separately.

Here’s an example of results obtained from a hotel review we found online;

Screen Shot 2016-11-22 at 17.58.05

Note how each aspect is automatically extracted and then given a sentiment polarity score.

Click to learn more about Aspect-Based Sentiment Analysis.

Sentence-Level Sentiment Analysis (SLSA)

Our latest feature breaks down a body of text into sentences and analyzes each sentence individually, providing sentiment polarity for each.

SLSA in action

Sentence-Level Sentiment Analysis is available in our Google Sheets Add-on and also through the ABSA endpoint in our Text Analysis API. Here’s a sample query to try with the Text Analysis API;

Now let’s take a look at it in action in the Sheets Add-on.

Analyze text

We imported some hotel reviews into Google Sheets and then ran an analysis using our Text Analysis Add-on. Below you will see the full review in column A, and then each sentence in a column of its own with a corresponding sentiment polarity (positive, negative or neutral), as well as a confidence score. This score reflects how confident we are that the sentiment is correct, with 1.0 representing complete confidence.

Screen Shot 2016-11-23 at 17.54.55

Analyze URLs

This new feature also enables you to analyze volumes of URLs as it first scrapes the main text content from each web page and then runs SLSA on each sentence individually.

In the GIF below, you can see how the content from a URL on Business Insider is first broken down into individual sentences and then assigned a positive, negative or neutral sentiment at sentence level, thus providing a granular insight into the sentiment of an article.

SLSA

What’s the benefit of SLSA?

As we touched on earlier, sentiment analysis, in general, has a wide range of potential use-cases and benefits. However, Document-Level Sentiment Analysis can often miss out on uncovering granular details in text by only providing an overall sentiment score.

Sentence-Level Sentiment Analysis allows you to perform a more in-depth analysis of text by uncovering the positive, neutral and negatively written sentences to find the root causes of the overall document-level polarity. It can assist you in locating instances of strong opinion in a body of text, providing greater insight into the true thoughts and feelings of the author.

SLSA can also be used to analyze and summarize a collection of online reviews by extracting all the individual sentences within them that are written with either positive or negative sentiment.

Ready to get started?

Our Text Analysis Add-on for Google Sheets has been developed to help people with little or no programming knowledge take advantage of our Text Analysis capabilities. If you are in any way familiar with Google Sheets or MS Excel you will be up and running in no time. We’ll even give you 1,000 free credits to play around with. Click here to download your Add-on or click the image below to get started for free with our Text Analysis API.

 




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At AYLIEN, we’re constantly working to improve and enhance our product offering, we have a long list of features and enhancements we’re working through and adding to every day. A lot of these features focus on one key aspect which we feel is pivotal to our success, Developer Experience.

Part of our, “mission” (insert fluffy corporate mission here) is to bring text analytics to the everyday man. To do this, we realised our API needs to be super easy to use and extremely easy to integrate with on top of that our documentation needs to be clear and simple, our support needs to be on point and above all we need to be “developer focused”.

We want our API to be all of the following:

  • Simple
  • Hackable
  • Selfservice
  • Developer Focused

One of our KPI’s we track quite closely is “time to first hello world” (TTFHW) a nice idea we came across in a recent SlideShare by John Musser from ProgrammableWeb..

With all of this in mind we are constantly iterating or sign up process to ensure it’s as easy as possible for developers to harness the power of our API. One of these initiatives we have been working on recently is to provide SDKs for popular languages that our users can use to get up and running with the API.

As Parsa, our founder put it; “We believe that many industries and businesses could benefit from NLP and Text Analytics, and we’ve made it our mission to make it easier for everyone to tap into these technologies to create smarter and more efficient tools and applications that will change the way their industry works. Our SDKs are an important step in this direction, and enable any developer to add the smarts of AYLIEN’s Text Analysis API to their applications.”

Our first batch of SDKs focus on the most common languages used by our users and can be downloaded from our GitHub Repo.

We plan on adding SDK’s for Java, C# and Go and we should have these ready early in the new year. Keeping in line with our focus on developer experience, we also plan on launching a series of sample apps and a sandbox coding area for developers to test the API in.Happy Hacking!

 





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