Naslov
Classification of Emotions Based on Text and Qualitative Variables

Autori
Dobša, Jasminka ; Šebalj, Domagoj ; Bužić, Dalibor

Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni

Izvornik
Proceedings of 44th International Convention MIPRO 2021, Data Science and Biomedical Engineering / Skala, Karolj - Rijeka, 2021, 412-417

Skup
44th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO 2021)

Mjesto i datum
Opatija, Hrvatska, 27.09.2021. - 01.10.2021

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
emotions ; text classification ; logistic regression ; deep learning

Sažetak
The aim of the paper is to compare the performance of classification of samples based on selfreporting emotions of subjects described by qualitative variables and textual description of situation in which it is experienced. The ISEAR data set was used in the research, which consists of 7666 samples classified in seven classes representing basic emotions (joy, fear, anger, sadness, disgust, shame, and guilt). For the text- based classification logistic regression (LR) was used as well as deep learning methods of convolutional neural networks (CNN), long short- term memory networks (LSTM) and convolutional long short-term memory networks (C-LSTM), while for classification based on qualitative variables LR and multilayer perceptron (MLP) were used. Performance of classification according to the textual description is similar for the LR and deep learning methods ranging from 53.65% in accuracy for CNN to 57.43% for LSTM, while the LR (accuracy of 77.19%) outperforms the MLP (accuracy of 51.83%) in classification according to qualitative variables. Also, we constructed an ensemble of classifiers based on text and qualitative variables showing improvement in performance compared to separate classifiers and accuracy ranging from 63.04% (CNN for text and MLP for qualitative variables) to 80.55% (LR for both text and qualitative variables).

Izvorni jezik
Engleski

Znanstvena područja
Informacijske i komunikacijske znanosti

POVEZANOST RADA