Emotionality is an extremely important, yet often underestimated part of the human being today. Emotions are a direct heritage of our distant ancestors – they were created to inform us about important events, to protect us from danger, to help us make decisions, to sustain us in action, and to establish and maintain relationships with others. Emotions translate into almost every area of life and without them it is simply impossible to function.

This (perhaps somewhat emotional) introduction is an introduction to a series of articles describing the possibilities of using modern technologies and artificial intelligence to recognize emotions and – in the perspective – to predict future decisions or consumer actions.

The source of information about these emotions can be, for example, posts of the Internet users themselves, published on social networking sites, discussion forums, in the form of comments under articles and wherever there is a possibility to express one’s own opinion. The more initiated will say – this is all about sentiment analysis! And they will be right, but only partially – we will look at these issues more broadly than just through the prism of sentiment. Because emotion is definitely not the same as sentiment, although we often see the two phrases used interchangeably.

What are emotions?

Let’s start by defining emotion. It is an instinctive, spontaneous, but also complex reaction of the brain to a stimulus, both external (perceived by the senses) and internal (memories, imagination or thoughts). Emotions are one of the components necessary for the perception of reality – they participate in cognitive processes, regulate social behavior, and in a healthy person – through mirror neurons – help to understand the emotional state of another person.

Why complex? The result of such a reaction is, among other things, the secretion of hormones and the expression of emotions through the body. Such patterns of physiological reactions as acceleration of breathing, pulse and heartbeat, pain, freezing, tingling or other arousal of the body, muscle tension, nausea, in addition to characteristic facial expressions, body language, sound properties of speech (prosody), etc., may appear in the expression. As a result – after collecting and processing the data, a specific action is taken, e.g. running away, putting up a fight, expressing admiration, appreciation, etc.

In the course of research on emotionality, it has been established (e.g. by Paul Ekman and Robert Plutchik) that some of the emotions occur in people irrespective of their settlement (or upbringing) in a given cultural circle. These emotions are the so-called primary emotions, and among them are: joy, sadness, anger, fear, disgust, surprise (Plutchik developing this theory additionally includes expectation and trust). Emotions can be compared here to colors: in both cases the basic components create their derivatives.

Thus, from the mixture of sadness and surprise comes disappointment, from anger and disgust – contempt, from trust and fear – submission, from surprise and anger – indignation. In a straight line – emotions are a way of expressing more complex constructs – feelings.

What is sentiment?

And how does sentiment relate to emotion? Sentiment can be defined as a peculiar mental attitude that is an aggregation of emotions and related thoughts. It is a decidedly subjective experience and develops from emotion only when we are aware of the presence of a phenomenon. Sentiment combines physiological reactions and cognitive components, and it occurs when we are already able to name the state we are in (or identify, name the emotion affecting us) and, consequently, decide to make a particular response. It will not develop unless it is preceded by the expression of an emotion.

Sentiment is, then, the result and, at the same time, the derivative of many emotions, an emotional state with a longer span of action than emotions – for it lasts as long as we are able to “cherish” its presence within us.

What is sentiment analysis?

Sentiment analysis in the context of Content Marketing is a way to measure and validate the overall impression, the mood that readers are in after reading given content. It doesn’t so much focus on the specific emotions expressed, but rather differentiates the feelings or impressions of the audience after reading the text into “positive” and “negative” (sometimes “neutral” as well) – this is reflected in the “positive” and “negative” sentiments. (sometimes also “neutral”) – this is reflected in the opinions expressed. When analysing the sentiment in the content, we collect data which, although highly simplified, gives us the possibility of quick estimation and easy processing. The analysis of the sentiment contained in the content is a method for measuring the reaction (attitude) of readers after reading, and thus the marketing value of the content itself.

How does this translate into action in practice? Thanks to the sentiment measurement it is easier for the publisher to verify particular articles – to check whether the reception of their message was positive or not; however, a problem may arise in the case of neutral, informative articles and content which will not lead to polarization of opinions when commenting on them. The data collected in this way is simply highly generalized.

Sentiment analysis provides insight into readers’ thematic preferences: it allows you to group content into those that evoke positive and negative associations. It makes it easier for publishers to plan the publication of content and allows for better (albeit not very precise) targeting of content audiences. Remember – the process of forming sentiment is long and complicated, and sentiment itself is susceptible to regulation and control by the reader’s thoughts. As a controlled reaction, it loses its natural dynamics and thus its authenticity.

Emotion analysis and the differences in the two solutions

Using the terms emotion and sentiment interchangeably is a mistake. As we have shown above, an analysis of the sentiment contained in published content is only a slice of a larger picture. A slice that describes a situation in which the reader will control his reactions and, as a result, consciously take action. So you could say that sentiment analysis is based on the logical actions of a given internet user.

Meanwhile, the analysis of emotions is essentially holistic – it focuses primarily on the primal, atavistic element, namely the instinct. Emotions appear spontaneously, they can be very intense and have a short-lasting effect, but they are also a motivator for specific actions. This is why an analysis of the emotions contained in a text, though more difficult to conduct, is in effect deeper and more accurate – it will make it possible to predict readers’ reactions, the actions they will take, or even try to identify the blockages holding them back from specific activity.

Analysing emotions allows you to gather a lot more data on the extent to which readers enjoyed, frustrated or simply found the text boring, which will then translate into more accurate planning of actions towards particular texts and predicting internet users’ reactions: sentiment will show us consumers’ attitudes towards the published content, while emotions – what’s behind these attitudes!

Example: Two reviews of a product are published. One of them says “This product did not meet my expectations,” while the other says “I hate this product with all my being.” Both descriptions will be classified as having a negative sentiment, but they will be significantly different in emotional valence – after all, there is a difference between disappointment or sadness and hatred. The analysis of emotions allows us to detect such, sometimes small, subtleties and indicate the specific types of behavior following them. It will help to guide the employees analyzing the data on what a given consumer may do after issuing such an opinion, or indicate how another consumer, whose product did not satisfy or saddened, will behave.

Let’s look at the results of testing the two sentence examples above with the Sentimenti text emotion analysis tool:

Ten produkt nie spełnił moich oczekiwań

This product did not meet my expectations

Nienawidzę tego wyrobu całym sobą

I hate this product with all my heart

anger / złość - 28%

anger / złość - 44%

fear / strach - 20%

fear / strach - 34%

oczekiwanie - 31%

oczekiwanie - 29%

surprise / zaskoczenie  - 40%

surprise / zaskoczenie - 44%

trust / zaufanie - 19%

trust / zaufanie - 20%

sadeness / smutek - 31%

sadeness / smutek - 45%

disgust / wstręt - 20%

disgust / wstręt - 34%

joy / radość - 20%

joy / radość - 22%

For comparison (study conducted with the same tool): sentiment for the first statement included in the sentence – positive – 20%, negative – 25%, emotional arousal – 46%. For the second – positive – 20%, negative – 40%, emotional arousal – 61%.

It is immediately clear that the analysis of emotions gives much more useful information. First comes anger – an obvious emotion, caused by discomfort, dissatisfaction with the purchase, but differentiated into disappointment and hatred. This anger is additionally boosted by fear, which in the second case reaches a significant intensity of 34%. Sadness is also significantly higher in the second statement.

What is not surprising is the similar level of surprise (discomfort in both cases) and anticipation (perhaps of a reaction from the brand or store below the post, or other comments similar in tone). The low level of trust is also not surprising, although it is not close to zero – probably both commenters are waiting for the mentioned reaction from the brand, and they have not experienced a similar situation from its side before. And the joy – you ask? Similar in both statements, it can mean hope for an apology or… satisfaction from expressed anger!

Another issue that makes sentiment analysis less useful is the possibility of data misinterpretation. What are we talking about? The misattribution of sentiment to emotion and the following consequences. An example from a popular fanpage of a well-known large discount retailer: “Why (angle grinder – author’s note) is not available stationary ?”

Conclusion: a consumer comments under a post in Social Media complaining that the product is not available in his local store. From the context of the comment it is clear that the sentiment contained in the comment is negative and the content itself (which can be seen even without the algorithm!) is characterized by sadness. Without analyzing the emotion, it is impossible to conclude that this sadness does not have to have a negative connotation – after all, it shows the brand’s communication tracking, indicates interest in the product and indicates a potential problem with its sale. Valuable information for analysts!

Let’s back this up with another example. We compare two comments and examine them in terms of the emotions and sentiment they contain.

I'm really regretting not being able to buy that blue cheese today that we like so much.


Bardzo żałuję, że nie udało mi się dziś kupić tego sera pleśniowego, który tak bardzo lubimy.

Today I saw cheese on the shelf at the store, all moldy, something disgusting.


Dzisiaj w sklepie widziałem na półce ser, cały spleśniały, coś obrzydliwego.

złość / anger - 38%

złość / anger - 70%

strach / fear - 26%

strach / fear - 39%

oczekiwanie / expectation - 31%

oczekiwanie / expectation - 21%

zaskoczenie / surprise  - 44%

zaskoczenie / surprise - 56%

zaufanie / trust - 23%

zaufanie / trust - 9%

smutek / sadeness - 41%

smutek / sadeness - 60%

wstręt / disgust - 28%

wstręt / disgust - 63%

radość / joy - 20%

radość / joy - 4%

Sentiment for statement 1: positive – 20%, negative – 35%, emotional arousal – 54%.

Sentiment for statement 2: positive – 4%, negative – 72%, emotional arousal – 64%.

Conclusions. In both cases, sentiment is rated as negative. But if we go into a deeper study (emotion analysis), we see that: confidence level is 2.5 times (250%) higher in statement 1 than in statement 2, joy is 5 times (500%) higher in statement 1, while anger is almost twice (84%) higher in statement 2, sadness is 46% higher in statement 2, and expectation (for something positive) is 47% lower in text 2.

Anger at an intensity of 70% is obvious for the second statement, as are the high scores for surprise, sadness, and disgust. Well, few people would be happy to see such a find in a store (joy 4% and trust 9% speak for themselves here). Analytically, however, statement 1 is much more interesting. What do we learn about the consumer who utters such a message?

We are struck by the high intensity of sadness and surprise, as it describes the state of the customer who has realized that he cannot fulfill his need. On top of that, there is a background of fear, probably caused by the fear of not having the favorite cheese in the future. The fear of shortages is accompanied by anticipation (will they deliver, won’t they?). Joy, on the other hand, is reflected in the fact of liking a certain kind of cheese, which is why it appears in the analysis in double digits.

Finally, we suggest looking at the table where we compare sentiment with specific emotions:















As you can see, assigning emotions from Plutchik’s model to only two types of sentiment can present difficulties when analyzing the data.

A few words of summary

“The pen is mightier than the sword”. – These were the words of Edward Bulwer-Lytton in his play “Richelieu”, and he was not wrong: words carry much more power than unreflective violence. No wonder scientists wanted to find out what lies behind words. What makes them appear at all and acquire some concrete meaning.

The first serious attempts to study sentiment began in the early 20th century, and had to do with studies of the polarization of public opinion. By the 1990s, there was already research into the subjectivity of textual content using computers, but the real explosion of research didn’t come until after 2004 – the year Google indexed 6 billion items, and Facebook debuted. There was research leading to the development of tools that could analyze sentiment. Who would have thought – almost 20 years have passed since then….

Today, content analytics technology has reached a new level: there is a shift away from simple, zero-one ratings of content to deeper analysis – emotion analysis. As a result, the scope of application of AI-based tools continues to grow. Today they can not only assess the mood of the consumer after a visit to the store; in this article we have used such examples because it is easy to show the principle of operation of analytical tools.

Tools for the analysis of emotions are already used in determining the behavior of investors on the stock exchange, prediction of stock prices or cryptocurrencies, they help in widely understood marketing activities, public relations (media monitoring) or customer service (e.g. analysis of communication with the chat-bot). There will be more and more applications, just as the technology itself will develop. Is it worth getting interested in? Definitely yes!