by Sentimenti Team | Aug 23, 2021 | SentiBrand
Getting to know consumer habits and understanding customer motivations is a good way to increase sales and grow your business. These goals can be achieved by analyzing emotions in the publications of Internet users, especially those related to your own brand. In this article we talk about the study of emotions in marketing and their impact on consumer behavior.
Emotions in marketing – are they worth exploring?
Emotions are responsible for certain body reactions and influence a person’s behavior. When analyzing emotions online, we use Robert Plutchik’s model, which distinguishes 8 basic emotions: joy, sadness, anger, disgust, fear, trust, surprise and anticipation. In expression, anger can lead to confrontation or aggression, joy enhances creativity and decision-making.
Sadness speaks of lack and manifests withdrawal, disgust rejects and stimulates flight, and fear also causes the urge to flee or attack. Trust speaks of closeness and relationship quality, surprise is an inducer of other emotions and feelings, and anticipation stimulates the body, preparing it for an upcoming event.
Since emotions are responsible for instinctive reactions, knowing their intensity can help predict consumer behaviour, improve customer service or monitor the brand image more effectively online. Knowing what evokes positive associations in customers, one can shape advertising, marketing and PR policy. Emotions are useful in creating characteristics of customers (persona) or for storytelling.
Researching customer emotions – advantages
Examining emotions in your own customers gives your company insight into consumer behavior. It will be useful e.g. when validating the effects of an advertising or communication campaign or in Content Marketing. It will help in creating tailored messages and more precise targeting of groups, more effective collection of leads, implementation of display campaigns, etc.
By examining the emotions of the recipients, the company may monitor its image and react in advance to the symptoms of crisis.
Emotion analysis versus sentiment analysis – the differences
The two terms are not used interchangeably; this is a mistake. Emotion analysis is the study of basic emotions – the unconscious, instinctual reactions of the brain and body to external or internal stimuli (e.g., thoughts, memories) and their effect on a person’s behavior. Meanwhile, sentiment analysis refers to reactions that are thought out and controlled by the subject: after becoming aware of the action of an emotion, he or she makes a decision that results in a mental attitude – sentiment.
Its study gives a result in the form of positive and negative sentiment, but we get an overall negative result without indicating the specific emotion and the behavior following it. With emotion analysis, we get the percentage score of each of the 8 emotions in the utterance, plus the sentiment analysis and the emotional arousal index.
They can check their own and their competitors’ emotionally charged phrases to determine how customers feel about their products or services, compare them with those of their competitors, and then make changes – to their offerings, their communication, or just their image.
Negative emotions: what can trigger them in marketing?
Negative emotions are conventionally called emotions whose perception is perceived as unpleasant. This perception gives the whole group of emotions a pejorative name, but the emotions themselves are warning signals of danger and are therefore not negative. In marketing or advertising emotions with such overtones may appear as a result of specific actions of brands or companies. What causes negative reactions of consumers?
For example, the use of the message, which strikes at the key values of the recipients of communication, breaks stereotypes, refers to unpopular views. Such slip-ups are the domain of multinationals that cannot predict the effects of actions in culturally distinct societies and do not take into account the mood of the target group. Another example is misalignment of the message with the requirements of the target group: it indicates misunderstanding of the group’s needs and will cause its frustration, which will translate into poor sales results and unfavorable comments on the Internet – and thus a scratch on the company/brand image.
Actions that cause negative emotions can also include overly intrusive PR and advertising, provocative actions (e.g. viral marketing), reprehensible practices towards employees, destruction of the environment, laboratory testing on animals, etc.
Negative customer emotions and their consequences
Customers most often talk about negative emotions through the company’s communication channels – social media, portals, e-mail. They comment under posts or create them themselves, review products and services, write opinions on forums, under articles, etc.
If the company’s message evokes negative emotions in them and these persist, their consequences will include negative WOMM (spreading unfavourable comments and opinions), brand switching (moving to the competition), brand detachment (severing relations with the brand), filing complaints, and even consumer boycotts, organizing protests or taking legal action.
How should a company respond to the negative emotions of its customers?
Negative emotions in your customers cannot be avoided, but you can minimize their effects. That’s why you should choose the analysis of emotions contained in the content. In social listening it will give you an up-to-date insight into the moods of your customers, in brand monitoring – the perception of the brand, it will also help to predict the behavior of consumers. And what to do when the symptoms of crisis appear? First of all, do not ignore them.
Negative moods will not subside on their own. Then accept the criticism and analyze the customer’s point of view – it is possible that the company’s policy was based on wrong assumptions. Finally – take concrete corrective actions, e.g. dialogue with the client, validation of communication or marketing activities, or improvement of the controversial service or product.
Interpretation of results:
The customer felt strongly surprised (62%) by the controversial statement of the maintenance department. She is angry about this (52%), but also feels anger about the careless finish of the apartment she bought.
The high level of surprise also relates to the individual faults that the customer mentions in her comment: the wrong way to suspend the ceiling, the faulty damp insulation around the chimney, the unprotected attic, the lack of a well-functioning but legally executed ventilation of the room with the fireplace or stove, the deficiencies in the electrical system and their repair that does not comply with building regulations, and finally the excessively high prices of additional services.
This state of affairs makes the customer feel sad (48%) and at the same time disgusted (loathing – 40%). The commentator does not know what else will happen to her in connection with the purchase (expectation – 35%), is afraid that things will not turn out well (fear – 36%), but still has hope for a positive outcome (expectation – 35%, trust – 21%). Finally, there is the indicator of joy (22%).
In the present case, it refers to ironic comments towards the developer, who is satisfied with his actions. We also have results for sentiment and emotional agitation – positive – 21%, negative – 45%, emotional agitation – 67%. This last element indicates that the commenter is highly agitated and inclined to take action.
What actions from such a client should be expected? Her emotional state, accompanying negative sentiment, and high arousal rate will likely push the commenter to post negative comments (negative WOMM) on forums, social media, and anywhere else she sees requests for feedback about this particular developer. In all likelihood, she will advertise any faults that arise with the developer, and possibly – pursue legal action.
Author: Igor Starczak. The publication also appeared in the quarterly “Developer & Marketing” (No. 3 / 2021).
by Damian Grimling | May 12, 2020 | Sentimenti research
The Sentimenti tools are evolving. At the moment we are entering into further and more advanced stages of tools development. In this article, however, we return to the roots of our project. It is worth mentioning what we actually measure and how do we understand particular emotions. And this is only the tip of an iceberg!
The easiest way to understand our concept is to use a ready-made description, created for the purposes of a certain project (whose Sentimenti tools are a tangible effect), developed by our researchers, Monika Riegel, PhD and Małgorzata Wierzba, PhD from Laboratory of Brain Imaging – Neurobiology Center, Polish Academy of Sciences.
Sentimenti tools. Definition of affective dimensions:
Valence, a sign of emotion and sentiment
- determines whether a given information or event evokes negative or positive emotions in us;
- has a range from negative emotions (caused by averse events) to positive emotions (caused by attractive events);
- the more positively we evaluate the information and events we experience, the more positive emotions are evoked in us;
- the more negative the information and events we experience, the more negative emotions evoke in us.
Emotional arousal
- determines the level of intensity of our emotions in relation to a given information or event;
- It ranges from no excitement (indifference) to strong excitement (agitation or excitement);
- Strong Arousal means a state of increased vigilance, attention and information processing;
- Arousal plays a key role in motivating our body to take certain actions;
- Arousal is also associated with specific physiological and neural responses (e.g. increased heart rate, accelerated breathing).
Basic emotions and their definitions
JOY
- Joy includes many positive emotions felt in response to what is known or new;
- we signal joy through a sincere, authentic smile, which consists of lifting the corners of the mouth diagonally upwards and tension in the circular muscles of the eye (lifting the cheeks and creating wrinkles around the eyes with age);
- joy is also shown by voice signals – breathing with relief or laughing or giggling;
- the main message of joy is “I like it”, so our support or encouragement for something.
SADNESS
- The feeling of sadness is a way to deal with loss and show others that we need support;
- Sadness signals include wrinkled lips (lower lip slightly raised and corners of the mouth lowered), inner corners of eyebrows joined and raised to the center of the forehead, raised cheeks;
- other manifestations of sadness are tears, as well as vocalisation expressing this emotion (weeping, trembling voice);
- The main message of sorrow is “comfort me”, and therefore an invitation to others to show us their support and care.
TRUST
- means believing that someone or something will behave in accordance with our expectations;
- feeling of trust brings us a sense of security and builds affection;
- The main message of trust is “I believe you won’t let me down”, it allows us to build not only intimate relationships with others, but also to find our place in society;
- Interestingly, we are more confident in faces similar to ours.
DISGUST
- The feeling of disgust with something allows us to avoid things that are harmful to us, both in the literal physical and mental sense;
- there are three elements of facial expressions expressing repulsion: the first is the ejection of the tongue reminiscent of spitting something out, the second is the lifting of the upper lip so that the gums and teeth are exposed, the third is the wrinkle of the nose and the expansion of the nostrils;
- the main message of disgust is to “go away from it”, which also signals to others to avoid the object of disgust because it is unhealthy, contaminated or reprehensible (socially or morally).
ANGER
- we feel anger when something blocks us or when we feel treated unfairly;
- when anger is uncontrollable, we raise our voice and scream, and when we have control over it, we take a sharp, attacking tone;
- the signal of anger on our face is a flash in our eyes, lowered eyebrows and squeezed lips;
- When people receive a signal of anger, they usually feel hurt and may try to take revenge by also showing anger;
- The main message of anger is to “get out of my way”, with a range from discontent to threat or attack, depending on the severity.
FEAR
- the fear of danger allows us to prepare for something that threatens us;
- and the most common signal of fear are wide open eyes, stretched lips and raised, joined eyebrows;
- the feeling of fear can also be accompanied by a reaction of avoidance. In example it is moving away from the source of fear, or dying;
- strong fear may be accompanied by an outburst of screaming, as well as signals such as heavy breathing, a slightly back-facing head and tense neck muscles;
- the main message of fear is “help!”, ranging from anxiety to panic, depending on the severity.
EXPECTATION
- an emotion involving excitement or anxiety in anticipation of upcoming events;
- Expectation is used to reduce the tension or stress associated with the challenge ahead by imagining it and developing a strategy to deal with it;
- The main message of the expectation is “I’m waiting for what will happen”. The ability to anticipate the effects of our actions in the future is essential for enjoying life.
SURPRISE
- emotion felt in reaction to unexpected events, expressing the discrepancy between our expectations and reality;
- the signs of surprise are raised and curved eyebrows, transverse wrinkles on the forehead, wide open eyes and enlarged pupils;
- is also visible through the lowered jaw, the separation of the upper and lower lips and teeth, the relaxation of the mouth
the surprise can be negative or positive;
- the main message of a surprise is “I didn’t expect it”. Although it ranges from light to very strong (the “run away or fight” reaction), depending on the intensity.
Emotion diads
Between 1960 and 1980, an American psychologist developed his theory of emotions. He decided to start with the eight basic emotions. According to Robert Plutchik’s theory of emotions, because it is referred to when different emotions are felt at the same time, they can create more complex types of emotions called diads. Diads arise from related but not opposing (mutually exclusive) emotions. The Sentimenti tools can analyze emotions based on 8 basic emotions. We stand out:
The basic diads (often felt):
- joy + trust → love
- trust + fear → humility, submissiveness
- fear + surprise → agitation, fear, horror
- surprise + sadness → disappointment
- sadness + disgust→ Repentance, repentance
- disgust + anger → contempt, envy
- anger + expectation → aggression, aggressiveness, aggressiveness
- expectation + joy → optimism
Secondary diads (sometimes felt):
- joy + fear → guilt
- trust + surprise → curiosity
- fear + sadness → despair
- surprise + revulsion → shock
- sadness + anger → suffering
- disgust + expectation → cynicism
- anger + joy → pride
- expectation + trust → fatality
Tertiary diads (less common):
- joy + surprise → admiration
- trust + sadness → sentimentalism
- fear + disgust → shame
- surprise + anger → indignation
- sadness + expectation → pessimism
- disgust + joy → pathology
- anger + trust → domination
- expectation + fear → anxiety
Opposites:
- joy + sadness → conflict
trust + disgust → conflict,
fear + anger → conflict
surprise + expectation → conflict
by Agnieszka Czoska | Apr 9, 2020 | Sentimenti research
Dr Jan Kocoń is a natural language engineer and the person behind the machine learning process within SentiTool, our solution for analyzing emotions in the text. Dr Kocoń coordinates the work of the linguistics team, integrates individual elements of the tool, and works closely with the IT team.
If you have to describe Sentimenti and the tools to anybody, what would you say first?
Sentimenti is a project meant to analyze emotions hidden in the text. Unlike competitive solutions that recognize the overtones of the text only (positive, neutral or negative), our tools manage to understand the text, assign specific meanings to the words in the text and name the certain emotions people feel about them. These emotions, in turn, provide the knowledge base for a machine learning mechanism that automatically recognizes emotions at the level of sentences and the whole text.
What does it mean that we analyse emotions in the text?
In the research carried out in our project we adapted the Plutchik model. It includes eight basic emotions: joy, sadness, trust, repulsion, expectation, fear, surprise and anger. We are able to estimate to what extent these emotions are expressed in the text.
How do we know what emotions people feel?
The knowledge base that helps our project includes more than 30.000 meanings of words, for which 20.000 unique respondents assign ratings for overtones and emotions. We are talking about “meanings” and not “words” on purpose, because words are ambiguous; for example “dark” means something different in “dark blue” or “dark people” and only in the latter case it carries emotions. Each meaning will ultimately receive 50 marks from different people. This allows us to know what feelings are evoked by certain meanings in the text. However, the emotion of the text is not a simple summation of the emotions assigned to the meanings in the text...
What else makes the emotion analysis tools in the text work?
Two things come to us to help. The first one is our gargantuan database of opinions. It came with associated overtones, derived from different areas: travel, medicine, products, services and more. We have over 10 millions of such texts in our database, which is an excellent source of information about the general feeling of the author. However, in order to find out what emotions a given text evokes in the reader, we also conduct our own research, analogous to research on single meanings.
This time the subject of these studies is the texts. The respondents attribute basic emotions to them, exactly the same way as they do with meanings of the words.
The second pillar of our Sentimenti tool is a combination of various machine learning methods. Experts in natural language processing provide us with tools for text analysis at the syntactic and semantic level, additionally they create rules for the analysis of meanings in context such as: negation, conjecture, weakening or strengthening of the overtones, etc. This is an additional help for automatic methods, such as deep neural networks, which are used to make the right conclusions about the emotions in the measured text.
What do you think automatic emotion analysis can be useful for?
Ultimately, I see many applications for our tools. The very first area that comes to my mind would be the marketing, or, more precisely, display advertising. This certain area covers the market of advertisements displayed in the context of web articles and is matching them with the emotions that the text of the publication evokes in readers. For example, in a sad text there could be an advertisement of an insurance company, and in a merry, joyful text there could be an advertisement for a trip.
Another area that we could cover is brand monitoring, i.e. analyzing how companies’ customers write on the Internet about a given company, its products and what emotions accompany them. Another interesting area could be sorting customers’ email complaints against the emotions contained in them, detecting conflicts arising in employee correspondence, detecting upcoming crises in Social Media, and even the possibility of diagnosing mental illnesses – the potential of Sentimenti tools is really huge!
What else do you plan to do in Sentimenti?
So far, there is a prototype ready with a simple text analysis on the level of meanings with an overtone analysis using our huge opinion resources. Currently in the Sentimenti team in Wroclaw I am managing to build a machine learning mechanism. It will make it possible to aggregate both information from the meaning knowledge base and information from the natural language processing stream. We are constantly receiving new data about the feelings of people reading certain texts, which are our teaching collection. The more data we gather, the better the quality of the tool there is.
by Sentimenti Team | Jun 6, 2019 | SentiBrand
Emotywne pozycjonowanie reklam, czyli targetowanie reklam wg emocji od niedawna nabrało nowego wymiaru. Okazało się, że komunikat można oprzeć nie tylko o kontekst, analizę ruchu na stronie, badania demograficzne, płeć i wiek ankietowanych, ale po dane sięgnąć też niemal w głąb ich serc. Jak to możliwe? Wystarczy rozpoznać ich emocje.
Emotional ad positioning and example: New York Times
In 2018, the New York Times conducted a study on the emotions of its readers. This was based on self-learning algorithms and combined with an analysis of feedback collected from readers about how they felt after reading the content of specific articles. The result of this research was an emotion prediction tool that indicates joy, sadness, hope, and 15 other emotions in readers, among others.
Not content to let this tool predict the emotions that NYT readers might potentially experience while reading future articles, the company went straight to selling advertising space. It was offered to owners of products with emotional character close to the emotions contained in the given articles. The possibilities turned out to be impressive: the tool made it possible to examine and create the emotional content of a given article and to better match the marketing message to it.
Marketing content so emotionally targeted and appropriately placed among other content achieved up to 80% better results than classic behavioural targeting (on average by 40%). The tool even made it possible to separate content with negative or disturbing undertones, so as not to add advertising messages that might fit the content or the reader’s profile, but are completely inconsistent with the tone of the text: New York Times.
Emotion targeting – emotional ads: perspectives
The agorithm can be applied not only to the articles contained on this site, but also to news and publications of other types. Therefore, it opened a whole new field for campaign creators. This resulted in 50 campaigns and over 30 million collected feelings, sentiments and emotions. Advertising messages were usually placed next to entertainment or corporate social responsibility content.
Interestingly, similar research was conducted in other editorial offices, including USA Today and The Daily Beast. The analysis was based on phrases (keywords) and emotions related to their meanings, and an attempt to answer the question of what mood current readers of a given text are in based on behavioral analysis of their actions on the website and frequency of returning to specific, emotionally charged content.
USA Today’s research has shown that readers don’t limit themselves to positive news, but read everything. This means you can target your message to them not only when the context is similar to the rest of the content, but also when readers are in a similar mood to the context of the content. Therefore, such a method allows you to more effectively create content for better communication of brands.
The Daily Beast, on the other hand, instead of trying to guess moods, indicates where on the site readers will spend the most time; in these popular places it tries to contextually place the marketing message. All based on positive emotions and negative emotions in advertising.
The future of the advertising market?
The described activities based on data analysis, algorithms and artificial intelligence are beginning to be the future of the advertising and public relations market. How do the market of ordering parties perceive these new solutions? It would seem that with such precise tools for targeting recipients, there is no need to worry about anything else. And yet opinions are divided.
According to some experts, basing a campaign solely on such “bought emotionality” is one-dimensional, restricts and narrows the field of activity and should therefore be associated with other methods of communication. On the other hand, it is an excellent solution for companies looking for safe solutions, making their marketing message more precise and targeting the most determined customers.
Sentimenti and emotional advertising. Identifying emotions in online advertising
Since the New York Times includes emotion ad positioning on its pages, the solution must work and be effective. Positive and negative emotions are taken into account. Is it possible to apply similar mechanisms in Polish?
Until now, this was not at all obvious. Algorithms for automatic processing of our language have been improved to such an extent that they are great for text analysis. But what about the emotions expressed in them? Until now there was no database of words, phrases or even whole texts written in Polish.
That is why the Sentimenti team created it. The database was created in the course of research we talk about on the blog and at academic conferences. It turned out that with good data it is possible to create an effective system of sentiment and emotion analysis and with it – ad positioning.
Interia Emotions – different emotions, one goal
We are now at a similar stage to where the New York Times was about a year ago. We have an application that efficiently analyzes text and the emotions it contains. We have started cooperation with Interia portal – we are creating an emotional map of its thematic services. From here it is only one step closer to taking the overtones of an article into account in ad positioning.
What is very important, emotive ad positioning does not mean additional duties for journalists. We will not tell anyone what emotions to express, because in practice each emotion creates an appropriate environment for ads. Text has a sad meaning? It is best to place an ad with ecological overtones. It expresses fear? This is a good context for pairing the article with an ad for insurance or dietary supplements.
The next step of the Interia Emotions project will be to investigate how exactly emotions in text react with ads. Therefore, when we check this, emotive and effective ad positioning will become a fact. Such a tool will certainly prove useful. Ads positioned based on the content of articles (rather than tracking the activity of Internet users) are less irritating for them.
by Agnieszka Czoska | Mar 14, 2019 | Sentimenti research
Each data analysis is aimed at understanding what information it contains. Has something changed, or is there a difference between A and B? Do changes in A correlate with those in C or D? Only these steps allow us to draw conclusions about the results.
First stage: measurement. What is the text?
The above statement also applies to the analysis of emotions or sentiment. Its first stage is the MEASUREMENT, checking how many and what kind of emotions we find in a given text or set of them. The result of a simple emotion measurement shows the intensity of each of Plutchik’s 8 basic emotions supplemented by positive and negative sentiment and arousal (overall emotional temperature of the text). Sometimes we can afford to interpret it already at this stage. We did it in one of our first entries, where we analyzed short ads (what is important, we have already managed to improve the way the results are presented). By analyzing the ads we wanted to show something characteristic for the whole type of texts: the most important emotions are joy and trust, only at the very beginning of the story about the product the creators allow themselves to remember the negative ones – to show the hardships of life before the era of the best shampoo or grease in the world.
The correct results of the Emotional Measurement are those that are consistent with people’s feelings, after all, each of us is an expert on feelings. Our tool owes its correctness to the participants of research on emotions in Polish, which we conducted according to the best scientific standards.
Measurement is only the first step towards understanding the message and the emotions it contains. When we deal with many similar texts or collections of texts, we have to do something else. We want to find out which shop has the best opinions? Which version of our marketing content expresses the most enthusiasm or best shows interest in the subject? Which of the texts in the “Beauty” section will delight, move or warn the reader? We are talking about COMPARISON.
The second stage: comparison. Does this text differ from the average?
Comparison is perhaps the most important stage in the analysis of emotions – thanks to it we not only find out what the text is like, but also how it compares with others. We can compare directly – as we did when writing about lipsticks and lipsticks. Then we were interested in which of the topics has an advantage in terms of positive emotions and whether this difference is statistically significant. However, comparing several or a dozen or so different cosmetic brands cannot be done in this way, it would not be the correct approach. That is why in the text about beauty companies we used a comparison to the average – we needed some kind of background measurement, so-called baseline. This approach will be useful, for example, when comparing shops and brands. We then answer the question which brand has better or worse results than most of the industry.
The most general type of baseline would be the sum of emotions that characterize not only the domain, portal or texts of a given author, but simply language. In linguistics, the so-called Polyanna effect is known, which is that there are more positive than negative expressions in every language. Not only in the dictionary, but also in what we say – this effect expresses quite a general tendency of our minds to spend time and energy rather on pleasant things. In our research we very often see this tendency – joys and trust are emotions that appear in the greatest intensity not only in advertising. The fact that language has its emotional mean all the more reason to draw conclusions only based on comparison and not on the measurement itself.
Third stage: trends. What do emotions do?
The analysis of emotions is also about tracking changes in time, i.e. monitoring emotions. We can check whether sadness or revulsion show a growing trend, i.e. there are more and more of them in statements on a given topic or in the opinions of customers. If we notice a trend, which is statistically significant, we can predict what will happen in the future and if by chance it does not mean an impending crisis (depending on the slope of the trend line).
At this stage it is also possible to go beyond the data from the Sentimenti tools. We started with something simple, accessible and yet untouched by others – we compared the emotional temperatures of mentions of listed companies with the prices of their shares, published publicly. Sentistock is great, it allows you to determine what the investor mood really is and how it translates into stock market fluctuations.
This part of the analysis of emotions depends entirely on who and for what purpose wanted to examine the overtones of the text, the notes, the conversation. We have also managed to show which emotions correlate positively with reactions on Facebook and Twitter – that is, how to write, so that the observers would like to like or comment on the post. However, we might as well ask how emotions correlate with remembering information from the text. Studies on the psychology of emotions, including those conducted by our colleagues from LOBI, indicate that the overtones of the text have an impact on what and how well we remember. Correlation between customer feedback and online store sales? Our tools are designed for this type of research.
Why so many stages?
Emotions were not created for themselves. This is our advisory mechanism: they tell us what action to take. Tversky and Kahneman did not receive the Nobel Prize for their research, but for showing that the consumer, including the stock market, is not rational. This statement tells us two things:
- emotions shape the market,
- we need good tools and methods to study this impact.
Trying to understand the emotions “on the eye” we won’t know more than the average customer wanting to buy a new computer, reading all the available reviews and then deciding on the brand for which he or she has (and had) the warmest feelings. Maintaining scientific standards, checking whether differences and trends are statistically significant and even better correlate with other, harder indicators is the best way to find out. After all, we live in the era of big data and data analysis.