Abstract
Emotion Distribution Learning (EDL) is a recently proposed multiemotion analysis paradigm, which identifies basic emotions with different degrees of expression in a sentence. Different from traditional methods, EDL quantitatively models the expression degree of the corresponding emotion on the given instance in an emotion distribution. However, emotion labels are crisp in most existing emotion datasets. To utilize traditional emotion datasets in EDL, label enhancement aims to convert logical emotion labels into emotion distributions. This paper proposed a novel label enhancement method, called Emotion Wheel and Lexicon-based emotion distribution Label Enhancement (EWLLE), utilizing the affective words’ linguistic emotional information and the psychological knowledge of Plutchik’s emotion wheel. The EWLLE method generates separate discrete Gaussian distributions for the emotion label of sentence and the emotion labels of sentiment words based on the psychological emotion distance and combines the two types of information into a unified emotion distribution by superposition of the distributions. The extensive experiments on 4 commonly used text emotion datasets showed that the proposed EWLLE method has a distinct advantage over the existing EDL label enhancement methods in the emotion classification task.
Highlights
Text emotion classification is an important research topic with many promising novel applications [1], such as emotional human-computer interaction [2], intelligent customer service [3], music emotion classification [4], anticipating corporate financial performance [5], and online product review analysis [6]. e goal of text emotion recognition is to find out the writers’ emotional states contained in sentences [1]
We compared the proposed EWLLE method with some state-of-the-art label enhancement methods, which are One-hot, MWLLE, and Label Enhancement (LLE). e comparative experiment worked in a pipeline way
The traditional single-label datasets were converted into emotion distribution labeled ones by label enhancement. en, the convolutional neural network (CNN) model was built on the enhanced datasets to predict the emotions, among which the highest one is selected as the final prediction
Summary
Text emotion classification (recognition) is an important research topic with many promising novel applications [1], such as emotional human-computer interaction [2], intelligent customer service [3], music emotion classification [4], anticipating corporate financial performance [5], and online product review analysis [6]. e goal of text emotion recognition is to find out the writers’ emotional states contained in sentences [1]. Few methods have been proposed to determine the emotion distribution from existing annotated datasets, which only contain single-labeled emotions. The MWLE method is proposed for facial emotion classification without considering the affective words that are effective in text emotion analysis. Zhang et al [24] proposed the Lexicon-based emotion distribution Label Enhancement (LLE) method, which generates emotion distributions from a single-label by introducing the linguistic information of affective words. We present a novel Emotion Wheel and Lexicon-based emotion distribution Label Enhancement (EWLLE) method, which calculates the psychological distances between emotions according to Plutchik’s emotion wheel and utilizes the linguistic information of affective words from some classical lexicons.
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