Exploiting Narrative Context and A Priori Knowledge of Categories in Textual Emotion Classification (2020)

Abstract: Recognition of the mental state of a human character in text is a major challenge in natural language processing. In this study, we investigate the efficacy of the narrative context in recognizing the emotional states of human characters in text and discuss an approach to make use of a priori knowledge regarding the employed emotion category system. Specifically, we experimentally show that the accuracy of emotion classification is substantially increased by encoding the preceding context of the target sentence using a BERT-based text encoder. We also compare ways to incorporate a priori knowledge of emotion categories by altering the loss function used in training, in which our proposal of multi-task learning that jointly learns to classify positive/negative polarity of emotions is included. The experimental results suggest that, when using Plutchik’s Wheel of Emotions, it is better to jointly classify the basic emotion categories with positive/negative polarity rather than directly exploiting its characteristic structure in which eight basic categories are arranged in a wheel.

## introduction understanding of narrative text requires the ability to read between the lines and determine changes in the characters' emotional states concerning the ongoing situation described in the text. given this motivation, @xcite proposed a framework for annotating fully-specified chains of mental states with respect to characters' motivations and emotional reactions. they publicized the resulting annotated corpus, called the story commonsense dataset. @xcite further demonstrated that the mental state of a character in a narrative can be more accurately classified by using the preceding context of a target sentence. although the results indicate the efficacy of the narrative context, the method might have certain limitations. the approach proposed by @xcite focuses on the character-specific context that selectively concatenates the sub-set of sentences in which the target character appears. although this account might be effective in excluding apparently irrelevant sentences, it may fail to extract implicit yet useful information from other parts of the context. thus, we adopt a simple yet presumably effective method that encodes the entire preceding context along with the target sentence using bert @xcite . the task of emotion classification is generally formulated as a multi-label classification task @xcite @xcite due to the innate complexity of human emotions: a sentence can express multiple emotions that an annotator may be unable to disassociate. among the issues linked to multi-label classification, a crucial one is the handling of mutual dependencies among emotion categories. @xcite proposed incorporating a term into the loss function so that it captures the interactions between a pair of plutchik's basic emotion categories @xcite . however, the reported evaluation results concentrated on a comparison with existing methods and did not disclose any outcomes that demonstrate the efficacy of the proposed loss function. we propose a multi-task learning method that jointly learns to classify plutchik's eight basic emotion categories and the positive/negative polarity of emotions. our overall experimental results show that both the narrative context and a priori knowledge regarding the emotion category system are effective for increasing classification accuracy. however, the latter is not very significant, suggesting that further exploration into the nature of human emotions that might be latent in written text is necessary. ## problem formulation in the present work, we use the story commonsense dataset @xcite and compare several methods to identify the possibly multiple emotions of a human character that are explicitly or implicitly expressed by target sentences in narrative text. note that each classification method is applied sentenceby-sentence. ## related work the story commonsense dataset provides the annotations for characters' mental states, which include emotions as well as human needs. thus, this dataset provides an opportunity to investigate into a method to infer a character's underlying motivation to perform an action and the resulting emotional statuses. as these mental states are often implicit in text, the story commonsense dataset has facilitated research into the effective exploitation of commonsense knowledge @xcite . the prediction of a character's emotional state is usually formulated as a classification problem @xcite , but @xcite framed it as a zero-shot commonsense question-answering problem, where the use of dynamically generated commonsense knowledge graphs was specifically focused. we thus highlight the former work in the following, which shares the research motivations with the present work. similar to the present work, @xcite exploit information or knowledge obtained from the emotion category in the process of emotion classification, but in different ways. they firstly introduced emotion label embeddings which are made from emotion label name to incorporate relevant semantic information. they further devised a clever way to incorporate semantics from the "emotion-label sentences" by using a pre-trained bert model. these devices improved both precision and recall. besides, they modeled the transition of emotion by observing label correlations, which significantly contributed to improving the recall. as a result of these efforts, they successfully achieved state-of-the-art emotion classification performances on the story commonsense dataset. however, unlike the present work, they did not present any experimental discussions that try to explore the emotion classification system, in particular the wheel structure of plutchik's basic emotion categories. ## concluding remarks the contributions of the present work are threefold. (1) emotion classification accuracy can be considerably increased by using a bert-based text encoder that properly incorporates narrative context; (2) joint classification with positive/negative polarity would be promising for achieving higher classification accuracy; (3) it is partly demonstrated that the use of plutchik's wheel of emotions sufficiently captures the nature of human emotions. however, the experimental results suggest that a priori knowledge regarding the emotion category system has not been fully captured and incorporated into the classification model, showing that further investigations are necessary. there could be two ways to accomplish this. one way is to incorporate the ontologically organized knowledge about the cognitive structure of emotions with respect to events, agents, and objects @xcite . such ontological knowledge could be injected into a classification model, but it should be represented as a computable structure such as a knowledge graph. another, presumably more direct, way is to exploit a set of inferential knowledge of everyday commonsense that could directly connect an event with the participants' emotional states. in this regard, we will seek an effective way to incorporate commonsense knowledge resources such as atomic @xcite .