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