What is sentiment analysis?
Sentiment analysis or ‘opinion mining’ is the ability to determine the general feeling being conveyed by a communication. It is used to help assess whether the author may be conveying a positive, negative or neutral viewpoint. The technique uses natural language processing (NLP) to score words according to their meaning.
What is sentiment analysis used for?
There are many different applications for sentiment analysis. However its most common usage is to review customer communications. By identifying negative reviews, a business can improve its customer experience or product.
Sentiment analysis is routinely used to flag issues with customer service. There are many ways in which a customer might complain. It could be by email, telephone or even social media.
By applying this technology you get a 360 view of your communications. You can use it to identify reviews containing negative sentiments. This allows you to respond quickly to try to resolve the issue. Or it can be used to measure the outcome of a new product feature.
A brand’s reputation is more important than ever. Word of mouth and online reviews are commonly checked prior to making a purchasing decision. Any negative reviews can impact your business and damage your reputation. It can be used to monitor your communications and help ensure your brand remains strong.
How accurate is it?
The accuracy of the analysis will typically vary according to the data set. Context is hugely important. The nuances of different languages can often skew whether a message is perceived as positive or negative.
To improve the accuracy then training the model is crucial and so the larger your own data set, the better the end result.
Can I apply sentiment analysis to email?
The majority of tools currently available analyse social media communications. The reason for this is that disgruntled customers often turn to Twitter or Facebook to complain about bad customer service.
However sentiment analysis can be applied to any text data. This includes emails and phone call transcriptions. Indeed, there is real value in analysing these communications.
There are numerous examples of sentiment analysis tools being applied to e-mail correspondence. It has been applied to the infamous Enron data set to see if it could be used to predict it’s subsequent downfall.
How is Threads this technology?
As part of our future development projects, Threads is now routinely analysing all of its email and call data. This will enable us to better understand the challenges of this rapidly evolving technology and train our system to improve our own customer service and improve our product.
If you are interested in this technology and want to know more about how we can help you analyse your own email and call data, contact us at email@example.com.