Emotion Cause Extraction on Social Media without Human Annotation (2023)

Abstract: In social media, there is a vast amount of information pertaining to people’s emotions and the corresponding causes. The emotion cause extraction (ECE) from social media data is an important research area that has not been thoroughly explored due to the lack of fine-grained annotations. Early studies referred to either unsupervised rule-based methods or supervised machine learning methods using a number of manually annotated data in specific domains. However, the former suffers from limitations in extraction performance, while the latter is constrained by the availability of fine-grained annotations and struggles to generalize to diverse domains. To address these issues, this paper proposes a new ECE framework on Chinese social media that achieves high extraction performance and generalizability without relying on human annotation. Specifically, we design a more dedicated rule-based system based on constituency parsing tree to discover causal patterns in social media. This system enables us to acquire large amounts of fine-grained annotated data. Next, we train a neural model on the rule-annotated dataset with a specific training strategy to further improve the model’s generalizability. Extensive experiments demonstrate the superiority of our approach over other methods in unsupervised and weakly-supervised settings.

## introduction the emotion cause extraction (ece) task was firstly introduced by @xcite , which aims to identify the underlying causes of a given emotion expression in textual data. previous studies mainly focused on extracting emotion causes from news articles @xcite @xcite @xcite . one representative study among them is @xcite , which constructed a new * corresponding author. corpus based on sina city news. the corpus has attracted much attention in subsequent studies and become a benchmark dataset for the ece task. in addition to news articles, microblog has nowadays become an important platform for internet users to publish instant posts and share their personal opinions about hot events or topics, which contains a huge amount of subjective emotional expressions. tracing the potential causes behind these subjective emotions is helpful to obtain a deep insight into the public emotions, discover the essential causes of the public opinion, and provide an important basis for governments to promptly adjust their political strategies. however, the ece task faces significant challenges due to the wide range of topics, diverse domains, and the prevalence of informal expressions in social media. early studies aimed to address these challenges by approaching the task from a linguistic perspective and employing rule-based methods to detect emotion cause expressions on social media @xcite @xcite . although these rule-based methods are generally designed and can be applied to different domains or topics, their performance remains limited. some recent studies further employed statistical machine learning or deep learning models to extract emotion causes in social media. however, most of these studies primarily focus on training their models on small-scale manually annotated corpora in several specific domains @xcite @xcite . despite obtaining better extraction performance, these studies heavily rely on fine-grained cause annotations and are solely suitable for specific domains. due to the huge amount of data and diverse domains in social media, it is impossible to manually construct an annotated corpus for each domain when we build a machine learning-based ece system, which greatly limits the large-scale applications in real-world social media scenarios. to address the aforementioned problems, in this work, we propose a new approach to extract emotion causes on social media without human annotation. our framework is centered around a rulebased method, bolstered by a specialized training strategy. firstly, a constituent-based rule (cbr) method is proposed to extract the emotion causes by utilizing the syntactic patterns in emotion and cause expressions, and obtain a large ruleannotated dataset without relying on human annotation. secondly, a rule-guided pseudo supervised learning (rgps) framework is introduced to develop a general system for emotion cause extraction on social media. this method involves training a model on the rule-annotated dataset by masking the cue words and includes a label refinement module for iterative learning. in this work, cbr is a rule-based method that relies on the constituent syntactic structure in the chinese language. unlike previous methods that mostly utilized word-level patterns of emotion cause expressions, cbr employs carefully designed rules based on the constituency parsing tree. this approach effectively improves the performance of span-level ece and achieves high precision in extraction. we employ cbr to a large-scale unannotated corpus to automatically obtain ruleannotated data. with such a rule-annotated dataset, we then train a neural model for extracting emotion cause spans based on a pre-trained language model. we propose to alleviate the problem of overfitting inherent rule patterns by masking significant rule features such as causal cue words. additionally, we propose a label refinement module to enhance the diversity and accuracy of data labels through iterative training, which enables the model to further improve its extraction performance and generalization ability. to evaluate the effectiveness of our approach, we construct a new emotion cause dataset, covid19-ece, which focuses on the topic of the covid-19 pandemic. we conduct experiments on covid19-ece and another social media ece dataset named coemocause @xcite . our experimental findings are as follows: 1) compared to previous rule-based methods, our proposed constituentbased rules demonstrate a significant enhancement in span-level emotion cause extraction performance. moreover, this approach achieves high precision, which is highly beneficial for large-scale practical applications in social media. 2) the pseudo-supervised ece model significantly improves the recall of emotion cause extraction based on cbr, which leads to a noteworthy improvement in the f-score. based on the rule-based pseudo annotation of 25,600 instances, our rgps approach achieves comparable span-level extraction performance to standard supervised learning methods that uses hundreds of human-annotated instances. 3) by leveraging our rgps approach and incorporating a small amount of human-annotated data, we achieve further improvements. e.g. with the help of 200 instances of human-annotated data, our approach yields results that are comparable to those obtained using full human annotations on that dataset. ## approach traditional ece aims at extracting emotion causes at the clause level @xcite . several recent studies extend the task to extract the fine-grained span-level causes @xcite . due to the short and informal nature of social media posts, span-level is more suitable for identifying the emotion causes in social media texts. therefore, this work focuses on the span-level ece task @xcite , which is formalized as follows: given a post s containing a sequence of n tokens @xmath0, w 2 , ..., w n ] and an annotated emotion expression @xmath1, e 2 , ..., e k ] in s , the span-level ece task aims to detect the boundaries of the emotion cause span from s, which stimulates the emotion expressions. in the upcoming sections, we introduce our emotion cause extraction approach, which includes two main stages: constituent-based rule (cbr) and rule-guided pseudo-supervised learning (rgps). ## related work early works on emotion cause extraction (ece) mainly focused on rule-based method. lee et al. in addition, they can't aware of the boundaries of the cause spans, which causes inferior performance when fine-grained ece is required. @xcite formalized the ece task as a clause-level binary classification problem and released a benchmark ece dataset collected from news articles. based on this corpus, many traditional machine learning methods @xcite and deep learning methods @xcite @xcite @xcite @xcite @xcite were proposed. xia and ding (2019) introduced a new task called emotion-cause pair extraction (ecpe) in news articles and many following studies have been proposed on this task @xcite @xcite @xcite @xcite . recently, some works proposed to extract emotion causes at the span level, and pointed out that the span-level cause is more precise than the clause-level cause @xcite @xcite . specific for the social media scenario, @xcite used topic modeling to extract word-level emotion causes in chinese microblogs. @xcite constructed a dataset with multiple-user structure for cause detection in chinese microblogs. they proposed two cause detection tasks for microblogs (current subtweet-based cause detection and original subtweet-based cause detection) and used svm and lstm to deal with them. @xcite presented a joint neural network approach for emotion classification and cause detection to obtain the mutual interaction across these two sub-tasks. chen et al. (2018a) introduced a hierarchical convolution neural network (hier-cnn) to incorporate word contextual features and event-based features. li et al. (2020) proposed a bootstrapping method to extract covid-19 related triggers of different emotions on twitter. although the learning-based approaches achieve sound emotion cause extraction performance, they suffer from the dependence on significant amounts of domain-specific fine-grained human annotations to reach their full potential. ## conclusion in this paper, we explore how to build an emotion cause analysis system on social media without human annotation. first, we design a dedicated rule-based approach based on explicit causal cue words and constituency parsing tree, and then use it to annotate data on a large-scale corpus. on the basis of this, we introduce a strategy to alleviate the overfitting problem of the rule-annotated dataset and refine the labels during training to improve the generalization and scalability of our model. experimental results on two datasets demonstrate the effectiveness of our approach.