by Damian Grimling | Oct 24, 2023 | Politics and Social
The results of the October 15, 2023 elections have had a stimulating effect on the prices of many financial instruments. Sentimenti analysts surveyed with the proprietary tool SENTITOOL, the period from October 11 to 18, measuring the level of the investor confidence index, defined as the intensity of confidence emotions expressed in posts made on social media. The study was based on an analysis of 1,517 such posts relating to diversified financial instruments.
Changes in the trust index
The final days of the campaign and the ordered election silence in effect especially on Saturday, October 14 caused investors to take a distinctly wait-and-see position. This resulted in a drop in the daily confidence index by an average of 3 percentage points from the level recorded as recently as October 12. As expected, only the successively announced results on voter turnout and preliminary exit poll data caused a decisive change in the level of the confidence index, which reached an increase of 11 percentage points on October 15.
Such a change in sentiment has boosted financial markets. A prime example of such a revival was the WIG20 bourse, which had already risen 5.3% from its last quote on October 16. In the following days, too, a cause-and-effect relationship can be seen between the confidence index and the WIG20 bourse.
Confidence index on Election Day
There was an interesting development of the confidence index on election day itself,October 15. An hour-by-hour analysis shows what dynamic fluctuations it underwent. Thus, after the opening of polling stations, it had an upward trend, which was slowed down by the turnout data at 12:00 p.m. The subsequent data at 5:00 p.m., combined with exit poll data, resulted in a dynamic (by 11 percentage points) increase in the confidence index. The 9:00 pm hour brought preliminary exit poll results. This, combined with turnout data, further boosted the index by another 16 percentage points. The 23:00 hour brought a record high for the confidence index – a level of 71% was recorded. This data brought the average daily confidence index on October 15 to 32%. It was 11 percentage points higher compared to the previous day.
Whether the positive momentum generated by the election results will have a more lasting trend in the financial markets and the economy as a whole, time will tell. Perhaps it will depend, among other things, on the pace at which the election promises are implemented. Promises made by the coalition that will take over our country.
The analysis was carried out thanks to the materials provided, in cooperation with the IBIMS Institute for Internet and Social Media Research.
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 Agnieszka Czoska | Apr 9, 2020 | Sentimenti research
You’ve been in Sentimenti from the beginning. What was it like in 2016?
The business idea for the study of emotions in the text came from W3A.PL company from Poznan. After consultations with the environment of Poznań psychologists, cognitive scientists and linguists, a draft of the project for NCBiR (National Centre for Research and Development) was prepared and the search for subcontractors started. After estimating the market, it turned out that two units are capable of undertaking such advanced research work: LOBI IBD PAS and Language Technology Group of Wrocław University of Technology.
Once you got the grant, how did you start working?
As a research manager I was responsible for organizing the work of the team. It was important for me to combine the scientific teams of subcontractors and the business team into one team. The interface between business and science is not easy. In the Sentimenti team everyone – presidents, PhDs and MSc – speaks to each other by name, each person has the right to express their opinion and make decisions.
You are the research manager and scrum master of our team – how much did you have to learn to become one?
I learned the Scrum management methodology for R&D projects in the UK, where I worked in the Argument Analytics project conducted in cooperation with the University of Dundee and financed by Innovate UK, the British equivalent of NCBiR. I understood then that the key issue in the cooperation between science and business is good communication. A common team, preferably working in one place, frequent meetings and evaluation of results to check if this is really what we want – this is the heart of good projects. Many other R&D projects that I have observed did not achieve their goals precisely because of such a lack of communication.
How does the scrum method differ from your previous project experience?
I am a scientist and I have gained most of my experience in academic work and basic research. The transition to applied research was not easy, but I was given a lot by the British culture of openness, communication and respect – the values that are inscribed in Scrum and that we transfer to our team. The three pillars of Scrum are also important: transparency, inspection and adaptation. Transparency means that every person in the team – even new and unfamiliar with the subject – has access to all information (except, of course, confidential information). This helps a lot in overcoming crises, looking for a solution.
And what are inspection and adaptation?
An inspection is a frequent and short “review” meeting, during which we check what has already been completed, whether we do not have any obstacles that the project management should deal with, whether someone has too much or too little work. This helps to master the natural feature of research projects – unpredictability. When the results are different from we expected or when we get information from the business that a solution is not working – we can quickly adapt.
How do you see further development of Sentimenti?
In February, we have already finished our research work and moved on to development work, i.e. we use the collected knowledge and data in the work on Sentitol – our main tool for text analysis. Thanks to the fact that we use an iterative approach, we implement functionalities by adding them in subsequent versions of the product, and simultaneously – according to the Scrum methodology – we finish each Sprint (stage of work in Scrum) with a working product. At the moment, we have working software that recognizes eight emotions in texts in Polish, thanks to research on over 20 thousand people. This is already a solution that exceeds the scope of other solutions present on the market, and we are preparing two more versions.
In the next version of Sentimenti we will include a module using LSS (Lexical Syntactic Structures), i.e. elements of the language that affect the evaluation, e.g. good + no, + very, + a little. Then we will include a module that uses deep neural networks technology, or more precisely – BiLSTM (bidirectional long short-term memory neural networks), so that it can evaluate the emotions throughout the text immediately – and this is a unique solution on a Polish scale, but also worldwide. Our scientific publication about this module will be published soon.
Therefore, in the project we use fast prototyping, and in parallel to the work of the scientific team, the company implements any new solution for customers – because we have a great interest in our solutions. Thanks to this we have already achieved much better results (and faster) than we planned at the beginning.
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.
