by Sentimenti Team | Jan 15, 2021 | SentiBrand, Sentistocks
Cyberpunk 2077 has already premiered, and the emotions surrounding this event are extreme. The inspiration for this comparative analysis came from the comments surrounding a post made by Michał Sadowski, the creator of BRAND24, on his private Facebook profile. The entry refers to the reception of the game by Internet users. Our attention was drawn in particular to comments regarding the use by investors of sentiment as an analytical tool.
Cyberpunk 2077 and CD Projekt share price. Sentiment analysis a tool for investors?
Generally available media monitoring tools offer automatic sentiment analysis (quantitative) as standard. However, we should ask ourselves whether we can infer changes in emotions on the basis of such analysis? If the opinion was negative, how negative? Is it possible to draw conclusions about measurable parameters on the basis of such an analysis? Such a parameter is undoubtedly the price (course) of shares. But based on such a chart, was it possible to predict what would happen to the share price of CD Projekt?
Sentiment analysis indicates that there was a significant predominance of positive sentiment over negative sentiment (quantitatively) during the period 7-14 December 2020. Would this indicate that such sentiment was confirmed by the upward trend in the price of CD Projekt shares listed on the WSE? Below we present the share price chart for this company (creator of Cyberpunk 2077) during the corresponding period.
The share price chart shows the opposite situation from the sentiment chart. During the period in question (7-14 December 2020), CD Projekt’s share price recorded a significant decline, but the predominance of positive over negative sentiment visible in the tool did not indicate this at all.
Emotion analysis. Could you have predicted the stock market trend?
The answer is yes! Analyzing the intensity of the eight basic emotions (not sentiment) it was clear that two in particular stood out during this period. These were anger and trust. By observing the correlation between them we could see a specific trend behavior. We have additionally marked two key moments on the chart.
A 30% drop in the price of CD Projekt shares was recorded between December 7 and 12, 2020. Moreover, the period from December 17 to 19 last year saw another, this time 17%, price decline. It was during these time periods that the maximum divergence (i.e. the maximum difference in intensity) of the emotions ANGER and CONFIDENCE occurred.
The intensity of anger reached its maximum values with a simultaneous downward trend in the intensity of trust. What conclusion can be drawn from this? First of all the conclusion is that it was not the level of sentiment that signaled foreseeable drops of the company’s share prices. It was the emotions themselves, and more specifically the observation of changes in their intensity during a key moment for the company.
The analyzed case of the Cyberpunk 2077 game was a very simple case study for emotion analysis. It was accompanied by a huge information noise caused by the game’s release. The emotion analysis used in the SentiStocks tool therefore allows for very precise price predictions of various financial instruments. Therefore, its use can be a real support for investors in their decisions, and it can often help predict potential image crises.
At this occasion it is worth mentioning about even 85% effectiveness of emotion measurement in predicting the price of e.g. Bitcoin. Our tools perfectly cope with this. You can read more about this topic HERE.
by Sentimenti Team | Sep 14, 2020 | SentiBrand
Product reviews are extremely important for businesses. It doesn’t matter if it’s a local small business or a large conglomerate. Every business wants to climb to the top of the rankings on Google and inspire consumer confidence. It also wants online reviews to be only positive.
Product reviews and their value to your business
Product and service reviews that we come across online are very desirable for a business. They are the ones that increase search engine visibility and improve SEO. They also help build consumer trust and boost sales and conversions. They’re also a big advantage over the competition, as long as their content evokes positive emotions. The statistics speak for themselves. Reviews saturated with positive emotions can generate a lot of revenue. Products with an average rating of 5 stars receive up to 126% more orders than those with four stars.
On average, consumers read up to 10 online reviews before trusting a product or company. Up to 32% of consumers visit a company or product’s website after reading a positive review. SEO and SEO experts agree on this – generating online reviews is one of the top three best performing factors. 82% of consumers read online reviews of local businesses.
These reviews can provide the impetus for choosing between our company and a competitor. Online product reviews not only give consumers insight into the pros and cons of the products and/or services offered. They are also a great way to attract potential customers.
Online reviews and testimonials? The more of them, the better
While consumers will read many reviews before a brand inspires their trust, timing is crucial. Nearly half of consumers will only pay attention to reviews that have been written within the last 2 weeks and not a day more.
For many local businesses, the best place to build a review presence will be Google My Business. If you are dealing with a typical e-commerce business, you can build a profile on Trustpilot. Those in the hospitality industry can check reviews on TripAdvisor. Reviews can also be found on Facebook, Ceneo, GoWork and many other portals.
Analyzing online reviews, or what if there are already some?
You can do many things with online reviews. In addition to presenting reviews on your website, you can simply analyze their content and draw deeper conclusions. This allows you to go beyond star ratings and take your business to the next level.
Reviews can be a great source of information about your business or product. By analyzing the most common complaints about products, you can make improvements that will keep customers happy. The company through this will have better reviews in the future.
Review analysis, or the study of consumer emotions
Online reviews are an essential part of any business hoping to make a positive mark online and build consumer trust. Consumer reviews can be used to enhance brand reputation and improve services.
As an example, we used our tool to study emotions, arousal, and sentiment on several sample products. We took data from ceneo.pl from the last year.
The products were chosen quite randomly after taking into account only one parameter – a larger number of opinions in order to make the data representative. The group of tested products included several random phones, headphones and a water filter cartridge, cosmetics, medicine and a scooter.
- Apple AirPods 2 white (MRXJ2ZM/A)
- BRITA Maxtra Plus 5+1 pcs. Filter cartridge
- Apple iPhone 11 64GB Black
- Apple iPhone 11 Pro 64GB Star Grey
- Chlorchinaldin VP 20
- Xiaomi Mi Electric Scooter M365
- Long 4 Lashes Eyelash Growth Serum 3ml
- Xiaomi Redmi Airdots Black
- Samsung Galaxy A40 SM-A405 64GB Dual SIM Black
- Xiaomi Redmi Note 8 Pro 6/64GB Gray
- Xiaomi Redmi Note 8T 4/64GB Blue
These are probably not all reviews of these products, but this is mainly an example of a survey and how to analyze content from consumers. Below are the results:
As can be seen, the Brita Maxtra product received the highest intensity of positive emotion and favorable image sentiment in this analysis. In the group of randomly tested products, the Samsung Galaxy A40 received the worst score. The order of products was arranged according to the value of positive sentiment – from highest to lowest.
An analysis of the intensity of joy, trust and anger in the surveyed opinions shows that non-technological products (phones, handsets) have joy and trust much less. Phones are characterized here by more criticism. When it comes to emotional arousal, i.e. the strength of emotion and the type of words used to express it, the best performer is Chlorchinaldin VP-20, and the worst – again – is Samsung Galaxy A40. This may mean that the criticism was not so strong in the case of this device, but it outweighed the positive emotions.
Selected negative with highest anger intensity:
- Unfortunately, the headphones COMPLETELY do not stick in the ears, no matter what kind of covers you use, and the second day one of them fell out of the ear and got lost. DO NOT RECOMMEND. 200 PLN thrown down the drain.
- Impressed by the positive reviews, I bought them at a low price. Very stable hold in the ear, have a nice case but that’s actually the end of the advantages. The manufacturer did not include any instructions so I had to reach for “tutorials” and “unboxingi” on youtube. In vain – the left earphone did not want to pair with the right one. It is worth knowing that the left handset connects with the right one and the right one connects with the phone. Several reset attempts did not help.
- Chinese fakes. The headphones cannot be paired with each other, despite following many instructions from the net. Both headphones are detected as right. No instructions in the box. No cable.
- 100% counterfeit I own the original and from this store. These fakes are not even 50% of the capabilities of the originals. I do not recommend
Selected negative with highest joy intensity:
- I have been using Brita for a long time. I stopped buying water at the store (especially in plastic) a long time ago. We have great quality tap water. After pouring it through the brite I am at peace with the quality. I recommend it to everyone. Great product.
- Very good quality. Son satisfied. I recommend
- Good quality product, fast delivery, everything is fine – I recommend 😉
- I bought it for a gift. My boyfriend is very happy and praises it. I recommend
As you can see, Sentimenti tool analysis allows you to not only examine the emotion saturation of review content. It also allows you to quickly categorize reviews into positive and negative. This process gives quick (in just a few minutes) and precise results. The above sample survey is just a prelude to further in-depth analysis. This type of research is done by Sentimenti team as a part of SentiBrand service.
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
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 Agnieszka Czoska | Nov 8, 2019 | Conferences, Sentimenti research
Sentimenti = Emotions. We’ve previously discussed how to accurately analyze emotions with automated tools. Today, we’ll explain how we gathered the data that led to the creation of Sentimenti tools. This article is a guide to conducting effective research on the emotional meaning of texts.
The text was prepared for the GHOST Day machine learning conference and you can view the presentation in Polish.
Sentimenti. What emotions?
To train machine learning algorithms to automatically identify emotions in text, we first had to ask people how they feel. This seemingly simple question had to be broken down into several components.
First, what types of emotions should we consider? How many are there, and how do they differ? To answer these questions, we consulted the emotion specialists from the LOBI team. In psychology, there are various models of emotions, from simple to complex and multidimensional. We ultimately chose two models, which we now refer to as the sentiment and emotion models.
The sentiment model, based on Russell and Mehrabian’s 1977 paper, describes emotions along two axes: positive-negative and high-low arousal. As for the emotion model, we adopted the Plutchik model, both for its scientific robustness and because a portion of the Polish Slavic network had already been classified using it. This alignment allowed us to compare our findings with expert annotations, serving as a key test for accuracy.
What words?
Once we knew how to classify emotions, the next question was: what words to analyze? Our first step was focusing on the emotional meaning of words. We compared our findings with databases like WordNet and NAWL.
Our goal was to create a list of 30,000 words or meanings. Some words are ambiguous, with emotional tones shifting depending on context. For instance, “depression” can refer to both terrain and a mood disorder. We limited ourselves to a maximum of three meanings per word, each presented in context.
Thanks to the WordNet project, we learned that 27% of words have emotional meanings. These emotionally charged words took precedence in our analysis.
Sentimenti project: who participated?
To analyze emotional undertones in texts, we needed insights from a representative group of speakers. We worked with the nationwide research panel Ariadna to gather participants. Over 20,000 people took part in the study, providing data on at least 50 words each.
How We Collected Emotional Data
We designed a tool to assess word meanings on scales reflecting both sentiment and basic emotions. Participants evaluated words based on the emotional overtones they perceived in given phrases.
The study’s structure also considered participant fatigue. To ensure high-quality data, each person reviewed 150 words over three rounds, with breaks between rounds to maintain focus.
Beyond Words
Our next phase expanded beyond words to assess the emotional undertones of entire texts. Linguists have long known that the emotional meaning of a text goes beyond the sum of its words. The grammar and structure of a text also convey emotions.
For this phase, we analyzed existing reviews (e.g., hotels, doctors), as well as shorter forms like sentences and phrases. To ensure comparability with our word-level analysis, participants rated texts using the same emotional scales.
From People to AI
Sentimenti’s text classification tools now achieve high accuracy in identifying emotions, thanks to the solid dataset we built from word and text evaluations. While advanced neural networks may seem impressive, no AI can succeed without robust data to train on.
We’ve shared the details of our algorithm development both on our blog and in this scientific publication. Additionally, 20% of our word database will soon be published for researchers worldwide who study Polish emotions. This interactive database will have its own dedicated page, similar to NAWL’s list of affective words.
