How to Measure the Efficacy of Your Sentiment Analysis Model

Customers often experience several emotions while interacting with a brand. This is commonly known as customer sentiment, and understanding it is critical to improving your CSAT score.

Even if a consumer’s overall journey with you as a brand has been satisfactory, there might be some touchpoints that have significant room for improvement. But without any proper feedback, it becomes tough to determine what you can work on to provide an even better experience. This is where customer sentiment analysis kicks in. You can learn more about the same in this post.

Simply put, sentiment analysis helps businesses understand if consumers are reacting to them positively, negatively, or neutrally. Let’s take a deeper look at why it is important to understand and analyze customer sentiment.

Why is it Important to Measure Customer Sentiment?

Sentiment analysis not only helps better understand how consumers perceive your brand but also improves customer-focused KPIs. It outlines what they do and don’t like so that you can make smarter, data-driven decisions. Some important customer-centric KPIs are:

Sentiment analysis works like a charm to provide accurate inputs for these KPIs. They automatically identify the customer tone, emotion, and urgency of various tickets to assign them a positive, negative, or neutral tag.

If you are intrigued to know more about elevating customer experience with the help of sentiment analysis, this post will help.

Measuring the Performance of Your Sentiment Analysis Model

Although sentiment analysis tools are meant to help you determine how consumers view your brand, not all of them are good at what they do. So, if you’re going to use them, it is best to understand their approach to performance and accuracy measurement.

The method to calculate may vary in algorithms but the core concepts to evaluate them are the same.

To measure the performance of the model, there are some important metrics you need to consider; accuracy, recall, precision, and F1 score. In order to understand these better, let’s take a brief look at an example.

1000 patients were tested for a virus, of which 30 were positive. Now, if this result were to be given through a machine learning algorithm, it would be classified into the following categories:

The desirable results would be “true positive” and “true negative,” but due to misclassifications, the results can also include false positives and false negatives. This happens because no ML algorithm is perfect and sometimes the predicted result isn’t the same as the actual result.

Therefore, there are two types of values: predicted and actual. Predicted values are referred to as “positive and negative,” while actual values are often termed “true and false.”

These values make up the confusion matrix, a popular measure used to solve classification problems. Here’s a sample of the same.

Now, let’s take a look at the key metrics that must be tracked in a sentiment analysis model.

• Accuracy: Accuracy represents the number of correct data returned in an instance, over the total number of instances.

  Thus, the formula to calculate accuracy is:

          In the above-mentioned case, the accuracy of the model would be 0.96
          However, it is not a great measure when the data sets are unbalanced.

• Recall: Recall is calculated from a set of results out of a processed document. Lower recall translates to higher false negatives, while higher recall means fewer false negatives. Therefore, 1 is the ideal value for recall.

  Thus, the formula to calculate recall is:

• Precision: Precision measures the accuracy of a classifier, and should preferably be 1. Lower precision means that the number of false positives is higher, while higher precision translates to fewer false positives.

  Thus, the formula to calculate precision is:

            In the above-mentioned case, the precision would be 0.50