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.
