Feature update: Sentence-Level Sentiment Analysis is now available
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.