Now more than ever, businesses need to be closest to their customers to understand how to serve them better. Failure to do so, the competitor swoops in and takes away the dissatisfied customers.

There are technologies that allow customers to give their views on a brand or a product. For example, customers can express themselves through customer reviews, user-generated content, call center logs, and surveys.

💡 Read Consumer Behavior Analysis 101: How to Learn More About Your Customers

The challenge with such feedback is that, at times, it is overwhelming for humans to review each comment and make something out of it. This is why calculating customer sentiment scores is crucial for businesses. 

Determining customer sentiment towards a brand or a product is complicated. It involves a lot of artificial intelligence and machine learning technology. Therefore, you need to understand how these technologies work as a business owner. So let us dive deep into how to do sentiment scoring and how to calculate it.

What Is Sentiment Scoring? 

Sentiment scoring uses AI tools to determine customers’ feelings about a brand or product. NLP (Natural Language Processing) and NER (Named Entity Recognition) are two of the most used AI and machine learning techniques while scoring sentiment.

These technologies analyze the customer more comprehensively by calculating the polarity of the text, doing semantic clustering, quantifying the negations, lemmatization, and tagging parts of speech.

This is important because while the customer sentiment score is given in a spectrum represented by numbers, the customer feedback is given as statements. And the scale of these statements can grow exponentially. As you popularize your brand through social media, SEO, citations service provider, and so on, more people will interact with your brand. That means even more people will have something to say about you. 

Therefore, this technology analyzes these statements and quantifies the words and phrases used. The customer sentiment analysis model uses natural language processing, considers the context of the feedback and the emotion in the piece of feedback, and analyzes content from different languages. Only after this quantification can we get either a positive sentiment score, a negative sentiment score, or a neutral score.

Positive, negative or neutral

Initially, there were more traditional ways to do sentiment scoring. For instance, on social media, businesses would only rely on analytics that show the number of likes, shares, and comments. While this way of scoring gave a few insights, it could also be misleading.

The algorithm had to love the piece of content for it to do well on a given social media platform. Therefore, a piece of content can go viral on social media not because people love your business but because the content matches the algorithm’s needs.

With the current technology, performing sentiment scoring paints a better picture of what the market thinks of your product and brand. You get a wholesome picture of your business by analyzing what people say.

Read How to Conduct Sentiment Analysis to Improve Customer Experience?

Sentiment scoring can be as simple as qualifying your analysis results as positive, negative, or neutral. A positive score would mean that people say good things about your business. There are many such service and product reviews online, but here is an example.

positive sentiment score
Positive sentiment score

A negative score would mean that there are many complaints on your business, such as the one below.

negative sentiment score
Negative sentiment score

On the other hand, the neutral sentiment would mean the customer never praised your business, nor did they complain. Instead, they just maybe stated a fact related to your business. This type of review is rare as the people who tend to review businesses exist at the ends of the spectrum. Either really impressed or really disappointed.

Deciphering emotions

Sentiment scoring gets complicated if you want to pick out the emotions within a piece of text. For instance, the word kill could mean different things depending on the context. 

If a customer says, “The wait is killing me.” That would be a negative review. But if they say, “The business is killing it.” That would be a positive comment. To decipher such emotions from a text calls for complex machine learning algorithms.

With these complex sentiment analysis algorithms, sentiment scoring helps your know precisely what in your business is attracting the positive or the negative sentiment. For example, you might find two sentiments in one context. The customer might praise one thing and complain about something else.

By analyzing these comments, you can pinpoint what parts of the business are impressive and which are underwhelming. For example, a customer might say, “The food is great, but the service is pathetic.” You need a complex, accurate sentiment analy sis tool to decipher the sentiments found in that simple review.

Sentiment scoring is a little convoluted, but with the right sentiment analysis tools, you will gain valuable insights necessary for your business to grow.

Sentiment over time in media monitoring tool
Sentiment analysis in Mediatoolkit

How We Calculate Sentiment Score

We have discussed sentiment scoring and how much technology and variation go into the scoring. But how exactly is this done? How are the qualitative texts converted to quantitative data? There are three methods to do this. 

But before the calculations even start, the data has to be processed. There is a lot of data in the raw comments that does not serve any use for the algorithm. We need to serve the algorithm only the words that make a difference. Whether the sentiment is positive, negative, or neutral and whether the words show emotion.

There are a few processes that the raw data has to go through before the analysis. We shall use the example below from a Yelp review to understand the data processing.

positive sentiment score
Positive sentiment score

When such a comment is fed into the system, the first thing the system does is called tokenization. Tokenization is how the system separates each word into its tokens for analysis.

Text normalization 

After the words have been tokenized, the system performs text normalization. Text normalization is the system removing non-text items in the comment. For example, the system removes commas, full stops, exclamation marks, and other punctuation during this phase.

After all the non-text elements of the comments have been removed, the system breaks down all words into their stem words. For instance, in the example above, the word ‘breaks’ is read as ‘break.’ Likewise, the word ‘fixes’ is read as ‘fix.’

The last step of data pre-processing in customer sentiment score analysis is removing superfluous words. The above example includes words like ‘very,’ ‘every,’ and ‘can’t say enough.’

Finally, the text that would end up being scored in the sentiment analysis would look like this. “Amazing fast reliable honest fair love fix ‘right away’ ‘great service’ ‘love this business’” I know it is not congruent for human speech. Still, it has all the vital information needed to score a sentiment.

Using the final statement, how then do we calculate the customer sentiment score using the three methods?

Read Sentiment Tracking: How to Get Inside Your Customers’ Minds?

Word count method 

The word count method is the simplest method of calculating sentiment. While using this method, you use the lexicon of the words. Then, you add the number of positive words and subtract the number of negative words.

The number you get, if positive, the sentiment of the comment is positive, but if the number is negative, then the sentiment of the comment is negative. However, if the number is zero, then the comment is neutral.

The example above has positive words only, making the example a positive sentiment score of ten.

Length of the sentence method

This method is optimal for long chunks of text. This method incorporates the word count method, but the result is divided by the total number of words in the sentence. 

Using the above example, that would be ten divided by ten, making it one. A score of one is the best you can get. It is the perfect score.

Read YouTube Sentiment Analysis: A Detailed Roadmap on How To Conduct it

Ratio of +Ve and -Ve word counts

In this method, the number of positive words is divided by the number of negative words plus one. So, from the example above, it would be 10/0+1. The “+1” becomes helpful here in eliminating the zero division error. 

Using this formula, any result that returns one is the neutral result, and anything above one is positive and vice versa. 

In Closing

Determining your business’s sentiment score is fundamental to understanding what your customers feel about your business. You can gather customer feedback from various platforms like social media, your website, and even review sites.

There is immense technology that goes into sentiment scoring. Using the technology, you cannot only tell whether the sentiment is positive or not, but you can tell the emotion depending on the feedback context.

You can then use the information you get to adjust business strategies by learning what parts of your business need improving and which are thriving.

You have to ensure that you maintain a favorable business sentiment at all times.


Daryl Bush is the Business Development Manager at Authority.Builders. The company helps businesses acquire more customers through improved online search rankings. He has extensive knowledge of SEO and business development.

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