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.
by Agnieszka Czoska | Feb 7, 2019 | SentiBrand
Not only our analyses show from time to time that the more emotions we show, the more we receive from our interlocutors. What’s more, I think everyone knows the basic principle of advertising and marketing: emotions pay off because they generate reactions. On the wave of these beliefs, we decided to check whether actually popular Facebook and Twitter posts are more emotional than those that evoke less liking or commentary.
We used Facebook Insights and Tweet Activity analytics statistics. We focused only on those that tell us about the actual interest in a given post: the number of reactions (likes and comments). We ignored the number of page views and similar “big numbers”. – Emotions do not necessarily affect the algorithms of social media, but they should be the reaction of those who observe a particular channel. Where did we get the data from? Not from the Sentimenti website, we have too few fans so far. But we happen to be familiar with a certain pop-culture portal – the statistics are for posts from Nie Tylko Gry.
Emotions on Facebook
We have collected data from January 2019. The portal has more than 1700 viewers on Facebook, at that time it published about 3 posts a day, including multimedia (e.g. containing film posters), links to its pages, several links to films (mainly trailers). We did not analyze these types of posts separately, we would need much more material to do so. We focused only on the text. The posts evoked 20 likes or 23 reactions on average (3 comments on average). We filtered out the 20 most popular posts and the 20 with the least reactions.
In both types of posts we found less than 20% of emotional words, 15% in the most popular ones and 18% in the second group. This difference is not statistically significant, so the “amount” of emotions alone is not responsible for the popularity of the post.
If we look at the distribution of individual emotions, we can see that these two groups of posts differ most in the presence of sadness and trust. If we analyze 8 emotions together, the difference is not statistically significant, but already the set of trust, sadness and anger gives a result that shows the difference between the groups (Chi=13,945; p=0,002). Interestingly, in the most popular posts there are more negative emotions and less trust. Does writing about pop culture feed on criticism? Or maybe doubts about the quality of the promoted works?
Emotion on Twitter
We also analyzed the twitts of the portal, this time in December 2018 and January 2019. The portal has less than 200 observers on Twitter, published, as on Fecebook, about 3 posts a day: photos and links (mainly to its website). On average, they generated 4 reactions to the post (including: likes, comments and sharing). We filtered 60 of the most and least popular posts.
The percentage of words carrying emotion was slightly smaller for twitts, about 13%, and did not differ between the two groups of entries. Interestingly, longer twitts seem to be more popular than shorter ones. However, none of these differences are statistically significant.
Joy and trust seem to differentiate twitty most strongly. In fact, if we consider these two emotions and fear, we get statistically significant differences between the groups (Chi=8,569; p=0,014), while for 8 emotions the result is not significant and we can only talk about the tendency. In popular twitts trust is more often expressed, and less often – fear and joy.
Emotions in social media
As you can see, Twitter and Facebook posts from the Not Only Games portal we are analyzing show the opposite relationship between emotions and popularity, although on both platforms trust seems to be an important emotion for the audience. It is more important than the ratio of words that carry emotion to neutral. In the case of social media, therefore, the portal cannot simply talk about the influence of emotions on the popularity of an entry, no differences remain relevant if we compare all popular and less popular posts. We have to treat both portals as separate collections of texts to say something about evoking readers’ reactions.
What does this difference mean? Perhaps the author-reader relationship on Twitter and Facebook is different, but the administrator may also be key here – and for “Nie Tylko Gry”, someone completely different deals with each of these channels of communication with the recipients.
In this analysis we have shown that emotions, especially trust, influence the popularity of a post on a social network. At the same time, there is no uniform, valid for all media – on Twitter, positive emotions “won”, and on Facebook, negative emotions. It is also not enough to simply show any feelings – the proportion of emotional to neutral words was not important.
Getting to know your audience is crucial for promotion in social media. In order to encourage interaction with our post, we should find out what our readers, potential clients and supporters actually react to. Do they prefer a photo or text? Probably the first. But do they prefer joy, anger or trust? That’s what we won’t find out from the Facebook algorithms yet. As we have shown, these preferences depend on the communication channel and without reliable data analysis we will not be able to evaluate them.
by Agnieszka Czoska | Jan 23, 2019 | Sentimenti research
In this read we want you to meet the idea behind the unique solution that we came up with. It works wider than popular sentiment analysis tools used today for monitoring internet activities and more. Why? Instead of measuring sentiment, we focus on emotions. What does the emotion analysis offer that sentiment analysis does not? Why do we need as many as 8 emotions to describe, or even predict somebody’s state of mind, moods, future actions etc.? And what could you possibly learn whilst knowing them? Let us find out.
What emotions does Sentimenti analyse?
In Sentimenti we have implemented our tools for using Robert Plutchik’s model of emotions. It reveals 8 atavistic, adaptive emotions that help a human being (and any other life form) to survive and create the base for developing far more complex emotional experiences.
The Plutchik model contains two positive emotions: joy and trust, two ambivalent ones: surprise and expectation, and 4 rather, so to speak, negative: sadness, fear, repulsion and anger. Each of them has its meaning, leading the person into an action, praise, criticism, need or surprise. We discuss these emotions one by one.
What is the result? By analysing all the 8 basic emotions, we can precisely assess how much your brand differs in the eye of Internet users, trace down customer opinions on products and services, find out on searched phrases, respond quickly to a communication crisis or compare the company to competitors, amongst the others. But sometimes we also love to have some fun checking on the emotional change during and after popular TV shows display, examining stock market reports reactions, an impact certain political, social or cultural event has and so on.
Sentiment analysis of joy and sadness
Joy and sadness are like black and white, sweet and sour or light and the darkness – a simple division into positive and negative.
Joy reflects a wide range of positive reactions and is related with a smile, the moment when we simply like something. Understood this way, it seems to be similar to a “positive sentiment” (as interpreted by sentiment analysis tools), but the key here is the intensity. Commonly understood joy (“positive sentiment”) is associated with a strong positive reaction to something. Our solution focuses on and lets you find out about any praise components that influence the levels of joy. Including the everyday courtesy.
Sadness is opposite. It describes the loss, a sense of loneliness and the need of comfort, sometimes is even being referred to the person’s ask for help. Together with the joy it forms a contrasting pair in Plutchik’s emotion circle.
The Plutchik model has the psychological background. According to the researches, expressing both emotions at the same time with a similar intensity proves an internal struggle, a very mixed attitude of the text’s author to the subject. It means the person is in unhealthy or somewhat uncomfortable, confusing circumstances. The comparable intensity of positive and negative sentiment cannot be interpreted this way.
The interpretation of joy and sadness levels in this case are different than the levels measured by sentiment analysis tools. In Sentimenti we use two models of text overtones, so we can measure it all. We also count the relationship between different emotions and sentiment. A correlation (we use the r-Pearson measure here) greater than 0.6 is called strong, and close to 0.9 means that the two variables are basically the same. We always measure the relationship between joy and positive sentiment and sadness and negative sentiment for broader cntext and more precise data collection. Depending on the subject, positive or negative sentiment can take almost entirely from a single emotion, but also as the result of many different emotions being expressed by Internet users at once.
The results concerning sadness and joy do not have to be directly related to those obtained in the analysis of sentiment – which also shows that it is the result of many different emotions being expressed in the texts at the same time. We are able to tell you what possibly is behind the joy and sadness.
6 other basic emotions
So now you know the joy and sadness are not just a simple sentiment, but can be developed from other, even complex emotions. We have taken a binary model and put I against the one with 8 categories. The two are already described, where are the other six? We will show what they do and why they differ from simple positive or negative sentiment.
Trust is expressing a sense of security, expecting something to happen the way we want to. It also covers a sense of community, a closer relationship, finding one’s place in the group, a feeling of mutual similarity etc.
Repulsion is an expression of disgust, strong criticism and aversion. When showing it, to us something is outrageous, alien or immoral. It is a strong emotion connected with the reaction of rejection and avoiding something. Many offensive nicknames that describe socially unacceptable groups are associated with repulsion.
Anger – another strong emotion, and very negative one. Instead of avoidance, it is associated with attack and sometimes leads into the aggression. In this way we express threats, but also dissatisfaction. It is accompanied by curses and shouting (in speech, in writing it can be expressed with stronger words).
Fear, as in life, is accompanied by danger, threat and risk. It is associated with escape, a desire to hide. It is also a cry for help.
Surprise comes up when our predictions don’t come true or something unexpected just happens to us. When combined with joy, it gives admiration, and with sadness – disappointment. It can have very different intensity, from a slight surprise to (also associated with fear) panic.
Expectation – the last of the emotions we describe. The opposite of surprise because it means making predictions and believing that they will come true. It can therefore be associated with anxiety or excitement, so it is also ambivalent.
8 emotions make sense
Describing the overtones of a text, portal or brand awareness on the scales of 8 emotions gives you a lot of information. Sometimes only three emotions from the whole model may matter the most, but you will only learn this when having an insight into the whole experience’s spectrum.
Emotions in the titles of portals are very different in terms of the sound of the start page (more in this entry)
First, the sentiment analysis is something different from the emotion analysis, yet we are talking here about two complementing models. Based on the results of one of them, one can predict to some extent the results of the other. Emotional analysis offers a much better insight into what actually checked texts communicate and what kind of consequences they may bring on. For example, an anger is a better catalyst for conflict or popularity loss than sadness. A decrease in trust is more dangerous than a decrease in joy. The graph above shows how different emotions are placed on information portals depending on their character and target group.
Individual emotions are not only differently described by us, but also differ qualitatively in the reader’s perception of the text (we examined it on 22 thousand people) and in the intention of its author. More trust does not necessarily mean more joy – these are very different feelings and can sometimes turn out to be mutually exclusive. These are basic emotions, so they should be independent of each other. At the same time, they form a system and sometimes interact, even if one displaces the other. If you want to know more about emotion analysis, we have also written about go od practices to draw valuable knowledge from measuring emotions with Sentimenti tools.