Sentiment Analysis uses Natural Language Processing to identify important keywords in your customer reviews and assign a positive or negative sentiment score to each keyword. This functionality allows you to analyse your reviews based upon the actual content of each review and understand how your customers feel about different aspects of your brand. These insights provide a better understanding of your customers, helping you to deliver an excellent customer experience!
Natural Language Processing Algorithm
Sentiment Analysis is made possible through Natural Language Processing. This algorithm is designed to identify important keywords in your reviews and then based on the context (modifiers) determine a sentiment score between -90 and +90. Sentiment Analysis can help you understand the customer sentiment towards the most commonly mentioned keywords within your reviews, without having to manually comb through countless reviews.
Sentiment Analysis Dashboard
The Sentiment Analysis Dashboard is built to provide tools and insights necessary to drill down into specific keywords and see the associated customer sentiment and reviews. This is designed to give you easy access to deeply understanding the aggregate sentiment towards different parts of your consumer experience.
Sentiment Analysis Collection
A Sentiment Analysis Collection can be used to help organise and analyse a given group of reviews. Administrators can customise their own collections in order to see the sentiment analysis for a group of related keywords. For example, a bank could create a collection for "online banking" and have keywords like mobile app, bill pay, online banking. From there, the bank could see a series of metrics related to keywords in this collection.
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