Going deep: Improving Music Emotion Recognition with layers of Support Vector Machines

2016 ◽  
pp. 209-212
Author(s):  
Yu-Jen Hsu ◽  
Chia-Ping Chen
Keyword(s):  
Support Vector Machines ◽  
Emotion Recognition ◽  
Support Vector ◽  
Vector Machines

scholarly journals Text-Based Emotion Recognition in English and Polish for Therapeutic Chatbot

2021 ◽  
Vol 11 (21) ◽  
pp. 10146
Author(s):  
Artur Zygadło ◽  
Marek Kozłowski ◽  
Artur Janicki
Keyword(s):  
Support Vector Machines ◽  
Emotion Recognition ◽  
Machine Translation ◽  
English Language ◽  
Naive Bayes ◽  
Support Vector ◽  
Neural Machine Translation ◽  
Parallel Corpus ◽  
Vector Machines ◽  
Polish Version

In this article, we present the results of our experiments on sentiment and emotion recognition for English and Polish texts, aiming to work in the context of a therapeutic chatbot. We created a dedicated dataset by adding samples of neutral texts to an existing English-language emotion-labeled corpus. Next, using neural machine translation, we developed a Polish version of the English database. A bilingual, parallel corpus created in this way, named CORTEX (CORpus of Translated Emotional teXts), labeled with three sentiment polarity classes and nine emotion classes, was used for experiments on classification. We employed various classifiers: Naïve Bayes, Support Vector Machines, fastText, and BERT. The results obtained were satisfactory: we achieved the best scores for the BERT-based models, which yielded accuracy of over 90% for sentiment (3-class) classification and almost 80% for emotion (9-class) classification. We compared the results for both languages and discussed the differences. Both the accuracy and the F1-scores for Polish turned out to be slightly inferior to those for English, with the highest difference visible for BERT.

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