How a sentiment score improves your brand strategy

Traditional methods like star ratings and net promoter scores (NPS) are familiar ways we quantify customer satisfaction. But this is just the tip of the iceberg when it comes to customer sentiment.

Advanced technologies like sentiment analysis help you go beyond numerical metrics by analyzing qualitative data such as social media comments, survey responses and reviews. This approach to calculating a sentiment score gives you a more nuanced understanding of customer opinion and a north star for improving your offerings and brand strategies.

Read on to explore what a sentiment score is, the advancements in calculating sentiment scores and how we do it at Sprout.

What is a sentiment score?

A sentiment score quantifies the sentiment or emotion expressed in qualitative data such as customer feedback or social media listening. It is calculated through the process of sentiment analysis and measured within the range of -1 to 1. Negative one being the highest negative sentiment, 0 indicating neutral sentiment and +1 denoting the highest positive sentiment.

Sentiment scores inform you if the market opinion of your brand is positive, negative or neutral. Further analysis of the data gives you an in-depth look into how you can improve different aspects of your business like customer service, marketing content, products and after-sales service to ensure you’re nurturing brand loyalty and business growth.

Traditional approaches to understanding customer sentiment

The traditional approaches to customer sentiment analysis have mostly relied on quantitative metrics. These include:

Virality

Virality refers to the total number of social media engagements, such as likes, shares and comments your content or campaign has received. Virality is traditionally used as an indicator of how well your brand, campaign or marketing content is resonating with your target audience and the general public. It gives an overall view of customer preferences so you can make informed marketing decisions and alter your strategies accordingly.

Star rating

A star rating is a popular method of understanding customer sentiment and is widely used by brands to evaluate a product or service. Star ratings are typically provided within a range of 1 to 5 stars, with 1 indicating the lowest level of customer satisfaction and 5 denoting the highest. Sometimes star ratings also include comments that add additional context to the rating.

NPS

NPS is a quantitative metric used to measure customer satisfaction and a customer’s proclivity to recommend the brand to family and friends. The higher the rating, the higher the customer loyalty. NPS ratings are often on a scale of 0 to 10, with 0 denoting the lowest rating and 10 being the highest.

Unlike star ratings or virality, NPS metrics often group customers into three categories based on their ratings.

• Promoters (8–10): These are happy customers who will actively promote the brand through word-of-mouth, in reviews or social media comments.
• Passives (7-8): These customers are satisfied but are not likely to promote the product or service.
• Detractors (6-0): These are deeply unsatisfied customers most likely to post negative reviews and will likely deter others from considering the brand.

Customer satisfaction score (CSAT)

CSAT is a method used to measure how satisfied customers are with the products or services of a brand. CSAT scores are calculated by measuring the average rating customers provide. CSAT scales can vary, for example, they can be between 1 and 10, with 10 being the highest or 1 and 5, with 5 being the highest level of customer satisfaction.

CSAT surveys can be sent after a transaction or periodically to understand customer satisfaction with the overall brand.

New advancements in calculating sentiment score

Traditional calculations are focused on quantitative metrics from key performance indicators (KPIs). But to get a truly accurate picture of brand sentiment, you need to add qualitative data found in comments and feedback to the mix. Research shows that even if most businesses received positive star ratings between 80% to 100%, these ratings did not reflect on the success of the business. This is because people, in general, tend to give higher positive ratings than their actual experience. This leads to a sea of positive ratings, which skews the number toward a higher positive score.

Machine learning (ML) and AI tasks like named entity recognition and natural language processing( NLP) help overcome this challenge. They help you understand customer sentiment more contextually, enabling you to find patterns in customer opinions within the ebb and flow of brand perception across timelines and campaigns.

Sentiment mining intensity varies based on the methods used. The three main ones are:

• Document-based sentiment analysis

This approach gives you a general understanding of the negative, positive or neutral sentiment in a document. It is used for small, uncomplicated data sets.

• Topic-based sentiment analysis

This method is more nuanced, scoring sentiment by topic. The ML model identifies commonly occurring topics and themes in the data and then analyzes sentiment in them.

This approach helps marketers understand what customers, or the general public, like and dislike about their brand. Thus providing relevant, actionable insights from reviews, social media listening or customer care emails and comments.

• Aspect-based sentiment analysis

This is the most advanced method used for sentiment mining. Aspect-based sentiment analysis further breaks down topics to identify and search for aspects within them, and then applies semantics to provide a more complete picture of customer sentiment. For example, it can identify aspects such as “room service”, “bar attendant”, “reception” or “valet parking” from a topic classification on “customer service” in the feedback data.

This granular form of sentiment analysis pinpoints to brands exactly what needs to improve and informs the strategies needed to increase customer satisfaction.

Data processing techniques used to calculate sentiment scores

Calculating a sentiment score for use in AI marketing depends on many data processing tasks done automatically by an ML model, such as large language models (LLM). These tasks include:

Tokenization

Tokenization is the process of separating the text into individual words. All punctuations are removed and the string of text is stripped down to blocks of words. For example:

[ The stay was nice but my room was cold and we had to wait for hours for the hotel staff to adjust the thermostat, even though the hotel seemed empty. When we tried to call the reception to enquire, they seemed impatient and rude ]

Text normalization

In this stage, all duplicate entries are removed from the data so there is no data anomaly. In this case, the text string remains unchanged as there is no redundancy.

[ The stay was nice but my room was cold and we had to wait for hours for the hotel staff to adjust the thermostat even though the hotel seemed empty When we tried to call the reception to enquire they seemed impatient and rude ]

Word stemming

Word stemming refers to the process of reducing a word to its root. In this example, the word “hours” and “seemed” are converted to “hour” and “seem”.

[ The stay was nice but my room was cold and we had to wait for hour for the hotel staff to adjust the thermostat even though the hotel seem empty When we tried to call the reception to enquire they seemed impatient and rude ]

Stop-word removal

All superfluous words are eliminated so only named entities and words denoting emotions are kept.

[ The stay was nice My room cold and we had to wait for hour for the hotel staff to adjust the thermostat even though the hotel seem empty When we tried to call the reception to enquire they seemed impatient and rude ]

The resulting processed text now reads, [ nice room cold wait hour hotel staff reception impatient rude ].

Since each word has a numerical equivalent in the ML model based on the scale of their negativity or positivity, the processed data gives you a score based on the total sentiment average. When calculated using the Lexicon method, if the word “nice” is assigned a score of 1 for positive, while “impatient” is assigned -.05 and rude -0.7, the resultant sentiment score for the review would be -1, which equates to negative.

Conventional approaches to calculating sentiment scores

There are multiple ways to calculate a sentiment score, the most common being the Lexicon method, which uses a 1:1 ratio to measure sentiment. However, when it comes to complex data collected from multiple sources such as social media listening or customer review forums, more advanced techniques are needed. Below is a breakdown of these methodologies.

Word count method

The simplest way to calculate the sentiment score is based on the lexicon or word-count method as in the example above. In this method, the number of negative sentiment occurrences is reduced from the positive occurrences.

Formula: # negative words – positive words = sentiment score

Example: 1 – 2 = -1. 

Deducing sentiment score with the length of the sentence

In this method, we subtract the number of positive words from the negative words and divide the result by the total number of words in the review sentence.

Formula: # negative words – # positive words divided by the number of words = sentiment score

Example: 1 – 2 / 42 = -0.0238095

This system is often used to understand longer reviews and comments.

Since this method is used to analyze large amounts of data, the resulting scores can run into long fractions. When done at scale, this can result in difficulty comparing and understanding the sentiment values. To overcome this challenge, the resulting scores are multiplied by a singular digit so the values are bigger, thus making comparison easier.

Ratio of positive and negative word counts

This methodology is considered the most balanced for measuring the sentiment score in big data. The total number of positive words is divided by the total number of negative words and then added by one.

Formula: # positive words / # negative words + 1 = sentiment score

Example: 1 / 2 + 1 = 0.33333

The longer the review, the higher the count of positive and negative scores. This approach normalizes the total length of the text, making it especially useful in analyzing reviews of varying lengths. In this method, a sentiment score of 1 is set as neutral.

How we calculate sentiment scores at Sprout

Sprout’s sentiment model uses deep neural networks (NNs), and in particular, large language models. LLMs work by considering the context of the entire block of text, reading the words from left to right and from right to left using the Bidirectional Encoder Representations from Transformers (BERT) models from Google.

Given a data set of already labeled documents, an LLM automatically identifies the words, phrases and word/phrase ordering contributed to a block of text being tagged as positive or negative. It then assigns a weight (numerical value) to each token in a block of text. With these weights calculated, we determine the sentiment for new, unseen text and the probability that it is positive, negative or neutral.

The importance of sentiment score for brands

Sentiment scores help you quantify and evaluate different aspects of your brand, product and services, giving marketing, product and customer care teams actionable insights into how exactly they can pivot their strategies toward a successful trajectory.

Thanks to AI and machine learning, there are multiple tools that eliminate guesswork and give you an accurate picture of your brand sentiment within minutes. Take a look at these sentiment analysis tools we’ve curated to explore how you can reboot your brand strategy.

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