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_12 presented an annotation scheme for events that negatively or positively affect entities (benefactive malefactive events). Then @cite_0 constructed two sense-level lexicon of benefactive and malefactive events for opinion inference.
@cite_0 @cite_12
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This paper presents an annotation scheme for events that negatively or positively affect entities (benefactive malefactive events) and for the attitude of the writer toward their agents and objects. Work on opinion and sentiment tends to focus on explicit expressions of opinions. However, many attitudes are conveyed implicitly, and benefactive malefactive events are important for inferring implicit attitudes. We describe an annotation scheme and give the results of an inter-annotator agreement study. The annotated corpus is available online. Opinion inference arises when opinions are expressed toward states and events which positive or negatively affect entities, i.e., benefactive and malefactive events. This paper addresses creating a lexicon of such events, which would be helpful to infer opinions. Verbs may be ambiguous, in that some meanings may be benefactive and others may be malefactive or neither. Thus, we use WordNet to create a sense-level lexicon. We begin with seed senses culled from FrameNet and expand the lexicon using WordNet relationships. The evaluations show that the accuracy of the approach is well above baseline accuracy. Recently, work in NLP was initiated on a type of opinion inference that arises when opinions are expressed toward events which have positive or negative effects on entities (+ -effect events). This paper addresses methods for creating a lexicon of such events, to support such work on opinion inference. Due to significant sense ambiguity, our goal is to develop a sense-level rather than word-level lexicon. To maximize the effectiveness of different types of information, we combine a graph-based method using WordNet 1 relations and a standard classifier using gloss information. A hybrid between the two gives the best results. Further, we provide evidence that the model is an effective way to guide manual annotation to find + -effect senses that are not in the seed set. We address the problem of identifying important events in the past, present, and future from semantically-annotated large-scale document collections. Semantic annotations that we consider are named entities (e.g., persons, locations, organizations) and temporal expressions (e.g., during the 1990s). More specifically, for a given time period of interest, our objective is to identify, rank, and describe important events that happened. Our approach P2F Miner makes use of frequent itemset mining to identify events and group sentences related to them. It uses an information-theoretic measure to rank identified events. For each of them, it selects a representative sentence as a description. Experiments on ClueWeb09 using events listed in Wikipedia year articles as ground truth show that our approach is effective and outperforms a baseline based on statistical language models.