Sentiment analysis is the ultimate buzzword these days. Another name for it is opinion mining or Emotion AI, which is a method of analysing the text data to identify its intent (often classified as positive, negative, or neutral sentiment). This analysis examines text mined from a wide variety of sources like online forums, social media platforms, support tickets and chatbot conversations etc. Artificial intelligence and machine learning run natural language processing (NLP) algorithms to analyse all the data. A key insight that NLP unlocks for businesses is turning raw, unstructured text data into interpretable insights for businesses through sentiment analysis.

What are the benefits and use cases of Sentiment Analysis for businesses?

With an increased number of customers, the feedback for any product or service has reached a level where manually examining the data is impossible. This is where opinion mining becomes helpful as it is carried out by machine learning or deep learning algorithms so that data can be categorised by their emotional tone. It allows businesses to gather insights about customer behaviour, their needs and wants, their preferences about the products and overall decision process.

For example, a customer had an unpleasant experience with a particular product and wrote a review about it, this technique will be able to highlight that comment so that the brand can accordingly rectify the situation. By analysing the emotional tones in the reviews, it is also possible to circle out the most liked and disliked products or service. This way the brand can also change their business strategy to meet the demand.

Sentiment analysis helps companies in identifying the user’s emotion, that way the brand can accordingly curate the communication to meet the needs and eventually lead to a decrease in customer churn. Another example of successful implementation of sentiment analysis is in customer support companies wherein these tools can monitor phone calls or chat sessions to escalate any negative feedback from a consumer in the right department.

Traditional social media monitoring of measuring the amount of likes and comments indicate the engagement rate for a company but do not share the insights about a customers’ expectations from a brand. The most straightforward use of such analysis is the measurement of trends or general sentiment towards a brand on social media and looking at the words around it to gauge the emotion behind it.

For a company to succeed, it must be aware of how the marketplace is receiving their products and services. When making use of the right technology and applying it to key business drivers, sentiment analysis can turn out to be a powerful tool for steering companies and their individual business units to successful outcomes from each customer interaction. This can have a major impact on company’s top and bottom line depending on how effectively customer sentiment is understood or leveraged.