by Sentimenti Team | Jul 22, 2021 | Scientific publications
Place of publication:
Behavior Research Methods
Title:
Emotion norms for 6000 Polish word meanings with a direct mapping to the Polish wordnet
Authors:
Jan Kocoń, Arkadiusz Janz, Piotr Miłkowski, Monika Riegel, Małgorzata Wierzba, Artur Marchewka, Agnieszka Czoska, Damian Grimling, Barbara Konat, Konrad Juszczyk, Katarzyna Klessa, Maciej Piasecki
Abstract:
Emotion lexicons are useful in research across various disciplines, but the availability of such resources remains limited for most languages. While existing emotion lexicons typically comprise words, it is a particular meaning of a word (rather than the word itself) that conveys emotion. To mitigate this issue, we present the Emotion Meanings dataset, a novel dataset of 6000 Polish word meanings. The word meanings are derived from the Polish wordnet (plWordNet), a large semantic network interlinking words by means of lexical and conceptual relations. The word meanings were manually rated for valence and arousal, along with a variety of basic emotion categories (anger, disgust, fear, sadness, anticipation, happiness, surprise, and trust). The annotations were found to be highly reliable, as demonstrated by the similarity between data collected in two independent samples: unsupervised ( n = 21,317) and supervised ( n = 561). Although we found the annotations to be relatively stable for female, male, younger, and older participants, we share both summary data and individual data to enable emotion research on different demographically specific subgroups. The word meanings are further accompanied by the relevant metadata, derived from open-source linguistic resources. Direct mapping to Princeton WordNet makes the dataset suitable for research on multiple languages. Altogether, this dataset provides a versatile resource that can be employed for emotion research in psychology, cognitive science, psycholinguistics, computational linguistics, and natural language processing.
Link: ResearchGate
by Sentimenti Team | Jul 19, 2021 | In the media
Intro: The Sentimenti company has created a tool that very accurately analyzes the emotion of trends in financial markets – Sentistock. It very quickly found its application in trading financial instruments, i.e. stocks, para-currencies (Forex), but also cryptocurrencies and stock indexes. Unlike competing solutions, which only focus on determining whether the tone of a text is positive, neutral or negative, Sentistock is a quantum leap forward, as it is able not only to understand the text, but also to attribute to words the corresponding meanings, emotions, as well as people’s feelings towards them.
Author: Rafał Tomkowicz
Place and date of publication: Banking Magazine, 19-07-2021
Article Link (Polish)
by Sentimenti Team | Jul 15, 2021 | In the media
Intro: Sentistock from Sentimenti is a tool for emotion-based analysis of trends in trading various financial instruments: stocks, currency pairs (Forex), cryptocurrencies or stock indices. It’s a huge market with exchanges in almost every country: starting with the Warsaw Stock Exchange and ending with New York’s Nasdaq. Unlike competing solutions that only recognize the overtones of a text, i.e. the so-called sentiment (positive, neutral, negative), Sentimenti is able to understand the text and assign specific meanings to words and the emotions people feel about those meanings.
Author: Editorial
Place and date of publication: CEO.com.pl, 15-07-2023
Link to article (Polish)
Download article (Polish)
by Sentimenti Team | Jul 14, 2021 | In the media
Intro: The fund has backed Sentimenti, a company that forecasts bitcoin quotes, among other things, based on the emotionality of online postings, with capital. It is preparing it for another round.
Author: Mariusz Bartodziej
Place and Date of publication: Puls Biznesu, 14-07-2021
Article link (Polish)
Article to download (Polish)
by Sentimenti Team | Jul 2, 2021 | Market research, Scientific publications
Place of publication:
- Human Vaccines & Immunotherapeutics, 2021
Title:
Vaccine misinformation on social media – topic-based content and sentiment analysis of Polish vaccine-deniers’ comments on Facebook
Authors:
Abstract:
Introduction: Vaccinations are referred to as one of the greatest achievements of modern medicine. However, their effectiveness is also constantly denied by certain groups in society. This results in an ongoing dispute that has been gradually moving online in the last few years due to the development of technology. Our study aimed to utilize social media to identify and analyze vaccine-deniers’ arguments against child vaccinations.
Method: All public comments posted to a leading Polish vaccination opponents’ Facebook page posted between 01/05/2019 and 31/07/2019 were collected and analyzed quantitatively in terms of their content according to the modified method developed by Kata (Kata, 2010). Sentiment analysis was also performed.
Results: Out of 18,685 comments analyzed, 4,042 contained content covered by the adopted criteria: conspiracy theories (28.2%), misinformation and unreliable premises (19.9%), content related to the safety and effectiveness of vaccinations (14.0%), noncompliance with civil rights (13.2%), own experience (10.9%), morality, religion, and belief (8.5%), and alternative medicine (5.4%). There were also 1,223 pro-vaccine comments, of which 15.2% were offensive, mocking, or non-substantive. Sentiment analysis showed that comments without any arguments as well as those containing statements about alternative medicine or misinformation were more positive and less angry than comments in other topic categories.
Conclusions: The large amount of content in the conspiracy theory and misinformation categories may indicate that authors of such comments may be characterized by a lack of trust in the scientific achievements of medicine. These findings should be adequately addressed in vaccination campaigns.
Link:
Taylor & Francis Online
Citation BibTeX:
@article{doi:10.1080/21645515.2020.1850072,
author = {Krzysztof Klimiuk and Agnieszka Czoska and Karolina Biernacka and Łukasz Balwicki},
title = {Vaccine misinformation on social media – topic-based content and sentiment analysis of Polish vaccine-deniers’ comments on Facebook},
journal = {Human Vaccines \& Immunotherapeutics},
volume = {0},
number = {0},
pages = {1-10},
year = {2021},
publisher = {Taylor & Francis},
doi = {10.1080/21645515.2020.1850072},
note ={PMID: 33517844},
URL = {
https://doi.org/10.1080/21645515.2020.1850072
},
eprint = {
https://doi.org/10.1080/21645515.2020.1850072
}
}
