Kernel models for affective lexicon creation

Toward Transformer Fusions for Chinese Sentiment Intensity Prediction in Valence-Arousal Dimensions

“…A cross-lingual approach was used to train a linear regression model for valencearousal score prediction, in which the dimension scores of English seed words were regarded as the source language and their translated Chinese seed words were viewed as the target language [17]. The valence ratings of new words were estimated based on semantic similarity scores and a kernel model which was trained using least mean squares estimation [18]. A locally weighted regression method was proposed to improve linear regression to predict the valencearousal values of affective words [19].…”

“…This section describes the existing methods for sentiment intensity prediction, including lexicon-based [4,[10][11][12][13][14][15][16], regression-based [17][18][19][20][21][22], neural-network-based [23][24][25][26][27][28][29][30][31][32][33] and transformer-based [34][35][36][37][38][39][40][41][42][43][49][50][51] approaches.…”

“…In general, sentiment intensity prediction methods can be summarized into four types: lexicon-based [4,[10][11][12][13][14][15][16], regression-based [17][18][19][20][21][22], neural-network-based [23][24][25][26][27][28][29][30][31][32][33], and transformer-based [34][35][36][37][38][39][40][41][42][43]. The lexicon-based approaches provide baseline results for reference, while transformer-based models have usually achieved promising results in the valence-arousal dimensions [9].…”