Abstract. Sarcasm is a pervasive linguistic phenomenon in online documents that express subjective and deeply-felt opinions. Detection of sarcasm is of great importance and beneficial to many NLP applications, such as sentiment analysis, opinion mining and advertising. Current studies consider automatic sarcasm detection as a simple text classification problem. They do not use explicit features to detect sarcasm and ignore the imbalance between sarcastic and non-sarcastic samples in real applications. In this paper, we first explore the characteristics of both English and Chinese sarcastic sentences and introduce a set of features specifically for detecting sarcasm in social media. Then, we propose a novel multi-strategy ensemble learning approach(MSELA) to handle the imbalance problem. We evaluate our proposed model on English and Chinese data sets. Experimental results show that our ensemble approach outperforms the state-of-the-art sarcasm detection approaches and popular imbalanced classification methods.

Microblog has become a major platform for information about real-world events. Automatically discovering realworld events from microblog has attracted the attention of many researchers. However, most of existing work ignore the importance of emotion information for event detection. We argue that people's emotional reactions immediately reflect the occurring of real-world events and should be important for event detection. In this study, we focus on the problem of communityrelated event detection by community emotions. To address the problem, we propose a novel framework which include the following three key components: microblog emotion classification, community emotion aggregation and community emotion burst detection. We evaluate our approach on real microblog data sets. Experimental results demonstrate the effectiveness of the proposed framework.

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