Quick sentiment test: Does the word ‘okay’ have a positive, negative or neutral sentiment?
Without any context, it could be either positive, negative or neutral. The same word can be used in multiple contexts with a range of meanings – the wonders of language, right?
Only partially right. Sorting through these sentiments efficiently enough to intervene before a customer has a bad experience can be challenging. In a contact center, bad CX scores can ripple into product development departments, employee satisfaction, and much more.
So what is sentiment analysis anyway, and what is it important?
What is sentiment analysis?
Before we dive into why you need it in 2021 and beyond, let’s understand what we mean by sentiment analysis. Sentiment analysis (or opinion mining) is a natural language processing (NLP) technique used to determine whether data is positive, negative or neutral.
Sentiment analysis is often performed on textual data to help organizations monitor brand and product sentiment in customer feedback and understand customer needs.
Since customers express their thoughts and feelings more openly than ever before, sentiment analysis is becoming an essential tool to monitor and understand that sentiment.
Automatically analyzing customer feedback, such as opinions in survey responses and social media conversations, allows businesses to learn what makes customers happy or frustrated to tailor products and services to meet their customers’ needs.
Why is sentiment analysis so critical?
Sentiment analysis has become critical because it allows brands to understand the feelings and perceptions of their customers towards their business. Businesses can make quicker and more informed decisions by automatically sorting the sentiment behind social media conversations, reviews, and more.
Forbes estimates that 90% of the world’s data is unstructured; in other words, it’s unorganized. Vast volumes of unstructured business data are created daily: emails, support tickets, social media conversations, surveys, articles, documents and more. Historically it’s been near impossible to analyze the sentiment in all these data sources quickly and efficiently.
Sentiment analysis has many benefits that include sorting data at scale, real-time analysis, and more.
Here is a brief overview of some key benefits of sentiment analysis:
Sorting Data at Scale
There is simply too much data to manually sort through tweets, posts, comments, reviews, customer support conversations and surveys. Your business would come to a halt if you spent all day analyzing, but if you leave it unsorted, your business will come to a halt anyway. Sentiment analysis helps businesses process vast amounts of data efficiently and cost-effectively.
Real-Time Analysis
Sentiment analysis can identify critical issues in real-time. For example, is a P.R. crisis on social media escalating? Is an angry customer about to churn? Sentiment analysis models can help you immediately identify these kinds of situations so that you can take action right away.
A Consistent Criteria
Tagging text by sentiment is highly subjective, influenced by personal experiences, thoughts, and beliefs. By using a centralized sentiment analysis system, companies can apply the same criteria to all of their data, helping them improve accuracy and gain better insights.