The process of identifying opinions expressed in a piece of text, especially in order to determine positive or negative consistencies.
It’s our mission to show as many industries as possible the opportunity that sentiment analysis provides to their data analysis processes. Previously, sentiment analysis was just confined to checking for certain words but with the recent advancements in Machine Learning, this has all changed. Our technology can now recognise in milliseconds whether or not a statement is positive, negative or neutral. On top of that, it can also detect the severity of the statement allowing us to highlight the most extreme reactions inside your data. To be able to do this, we’ve trained our own Machine Learning technology on more than 60 million individual pieces of feedback.
We believe that true sentiment analysis is the final step in the feedback cycle, not only should you be gathering individual feedback but you should be able to evaluate all of your feedback in seconds, instantly knowing what you’re doing right & what you’re doing wrong. That’s where we step in, our sentiment analysis platform sits simply alongside your data collection mechanics and returns to you, a live and dynamic, aggregation of all of your customers sentiment. If you want to find out more about how our sentiment analysis platform can help your business then click on the Get In Touch button at the bottom of this page.
WHY CHOOSE OUR SENTIMENT ANALYSIS?
We know there are a number of different sentiment solutions on the market, some coming from bigger names than us, so why do we think you chose our solution? It’s simple, we’re flexible. Our whole platform has been built to be as simple and scalable as possible, meaning less requirements on your development team to integrate and greater control over how often data is processed. This all means that you’re now able to introduce sentiment analysis to your businesses, products and services in a more streamlined manner reducing both time and cost resources.
- Boasts an accuracy rating higher than many competitors
- Isn’t reliant on keyword tagging
- Instantly recognises sentiment within body of text
- Returns severity of sentiment
- Trained entirely on customer feedback
- Underlying algorithms continuously monitored and trained based on new data
- Understands the structure in which customers leave feedback
- Returns sentiment rating between 100 (very positive) & -100 (very negative)
- User feedback fed back into system increasing accuracy