Many Natural Language Processing works on emotion analysis only focus on simple emotion classification without exploring the potentials of putting emotion into "event context", and ignore the analysis of emotion-related events. One main reason is the lack of this kind of corpus. Here we present Cause-Emotion-Action Corpus, which manually annotates not only emotion, but also cause events and action events. We propose two new tasks based on the data-set: emotion causality and emotion inference. The first task is to extract a triple (cause, emotion, action). The second task is to infer the probable emotion. We are currently releasing the data-set with 10,603 samples and 15,892 events, basic statistic analysis and baseline on both emotion causality and emotion inference tasks. Baseline performance demonstrates that there is much room for both tasks to be improved.
@cite_4 first proposed a task on emotion cause detection. They manually constructed a corpus from Academia Sinica Balanced Chinese Corpus. @cite_20 built a dataset using SINA city news then propose an event-driven emotion cause extraction method using multi-kernel SVMs. @cite_15 directly selected the emotions-directed frames in FrameNet to build an English emotion cause (or stimulus) corpus then used CRFs to detect emotion causes. Some study @cite_1 designed corpus through annotating the emotion cause expressions in Chinese Weibo and extended the rule based method to informal text in Weibo text. @cite_21 focused on current original-subtweet-based emotion detection and annotated a multiple-user structure. @cite_10 organized NTCIR-13 ECA (emotion cause analysis) task. It designed two subtasks including emotion cause detection subtask and emotion cause extraction subtask.
@cite_4 @cite_21 @cite_1 @cite_15 @cite_10 @cite_20
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Emotion processing has always been a great challenge. Given the fact that an emotion is triggered by cause events and that cause events are an integral part of emotion, this paper constructs a Chinese emotion cause corpus as a first step towards automatic inference of cause-emotion correlation. The corpus focuses on five primary emotions, namely happiness, sadness, fear, anger, and surprise. It is annotated with emotion cause events based on our proposed annotation scheme. Corpus data shows that most emotions are expressed with causes, and that causes mostly occur before the corresponding emotion verbs. We also examine the correlations between emotions and cause events in terms of linguistic cues: causative verbs, perception verbs, epistemic markers, conjunctions, prepositions, and others. Results show that each group of linguistic cues serves as an indicator marking the cause events in different structures of emotional constructions. We believe that the emotion cause corpus will be the useful resource for automatic emotion cause detection as well as emotion detection and classification. A notably challenging problem in emotion analysis is recognizing the cause of an emotion. Although there have been a few studies on emotion cause detection, most of them work on news reports or a few of them focus on microblogs using a single-user structure (i.e., all texts in a microblog are written by the same user). In this article, we focus on emotion cause detection for Chinese microblogs using a multiple-user structure (i.e., texts in a microblog are successively written by several users). First, based on the fact that the causes of an emotion of a focused user may be provided by other users in a microblog with the multiple-user structure, we design an emotion cause annotation scheme which can deal with such a complicated case, and then provide an emotion cause corpus using the annotation scheme. Second, based on the analysis of the emotion cause corpus, we formalize two emotion cause detection tasks for microblogs (current-subtweet-based emotion cause detection and original-subtweet-based emotion cause detection). Furthermore, in order to examine the difficulty of the two emotion cause detection tasks and the contributions of texts written by different users in a microblog with the multiple-user structure, we choose two popular classification methods (SVM and LSTM) to do emotion cause detection. Our experiments show that the current-subtweet-based emotion cause detection is much more difficult than the original-subtweet-based emotion cause detection, and texts written by different users are very helpful for both emotion cause detection tasks. This study presents a pilot study of emotion cause detection which deals with Chinese microblogs using a complicated structure. To identify the cause of emotion is a new challenge for researchers in nature language processing. Currently, there is no existing works on emotion cause detection from Chinese micro-blogging (Weibo) text. In this study, an emotion cause annotated corpus is firstly designed and developed through anno- tating the emotion cause expressions in Chinese Weibo Text. Up to now, an emotion cause annotated corpus which consists of the annotations for 1,333 Chinese Weibo is constructed. Based on the observations on this corpus, the characteristics of emotion cause expression are identified. Accordingly, a rule- based emotion cause detection method is developed which uses 25 manually complied rules. Furthermore, two machine learning based cause detection me- thods are developed including a classification-based method using support vec- tor machines and a sequence labeling based method using conditional random fields model. It is the largest available resources in this research area. The expe- rimental results show that the rule-based method achieves 68.30 accuracy rate. Furthermore, the method based on conditional random fields model achieved 77.57 accuracy which is 37.45 higher than the reference baseline method. These results show the effectiveness of our proposed emotion cause detection method. Emotion, a pervasive aspect of human experience, has long been of interest to social and behavioural sciences. It is now the subject of multi-disciplinary research also in computational linguistics. Emotion recognition, studied in the area of sentiment analysis, has focused on detecting the expressed emotion. A related challenging question, why the experiencer feels that emotion, has, to date, received very little attention. The task is difficult and there are no annotated English resources. FrameNet refers to the person, event or state of affairs which evokes the emotional response in the experiencer as emotion stimulus. We automatically build a dataset annotated with both the emotion and the stimulus using FrameNet’s emotions-directed frame. We address the problem as information extraction: we build a CRF learner, a sequential learning model to detect the emotion stimulus spans in emotion-bearing sentences. We show that our model significantly outperforms all the baselines.