Damian GrimlingSentimenti sp. z o.o. · B&R
Damian Grimling
Diplom
sentimenti.com | sentistocks.com
About
7
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Introduction
SENTIMENTI is a result of cooperation between linguists, information science and artificial intelligence based on deep neural networks. Algorithms available in our software have access to a unique database of emotional reactions of more than 20.000 people who took part in our research. Semantic and syntactical analysis helps us to distinguish subtle differences in meanings of words found in various contexts.
Emotive analysis covers 8 basic emotions and two scales of polarity and arousal.
Skills and Expertise
Publications
Publications (7)
In this article we present extended results obtained on the multidomain dataset of Polish text reviews collected within the Sentimenti project. We present preliminary results of classification models trained and tested on 7,000 texts annotated by over 20,000 individuals using valence, arousal, and eight basic emotions from Plutchik’s model. Additio...
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 data...
Many tasks in natural language processing like offensive, toxic, or emotional text classification are subjective by nature. Humans tend to perceive textual content in their own individual way. Existing methods commonly rely on the agreed output values, the same for all consumers. Here, we propose personalized solutions to subjective tasks. Our four...
Analysis of emotions elicited by opinions, comments, or articles commonly exploits annotated corpora, in which the labels assigned to documents average the views of all annotators, or represent a majority decision. The models trained on such data are effective at identifying the general views of the population. However, their usefulness for predict...
Presentation for the article: Propagation of emotions, arousal and polarity in WordNet using Heterogeneous Structured Synset Embeddings
In this paper we present a novel method for emotive propagation in a wordnet based on a large emotive seed. We introduce a sense-level emotive lexicon annotated with polarity, arousal and emotions. The data were annotated as a part of a large study involving over 20,000 participants. A total of 30,000 lexical units in Polish WordNet were described...
In this article, we present a novel multidomain dataset of Polish text reviews. The data were annotated as part of a large study involving over 20,000 participants. A total of 7,000 texts were described with metadata, each text received about 25 annotations concerning polarity, arousal and eight basic emotions, marked on a multilevel scale. We pres...
