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Multi-label classification in social network environments is becoming a key area of data mining research in recent years. Given some nodes' labels (i.e., the sources), the task is to infer some other nodes' labels (i.e., the targets) in the same network. Relational classification methods, which leverage the correlation of labels between linked instances, have been shown to outperform traditional classifiers...
A key question in sentiment analysis is whether sentiment ex-pressions, in a given text, are related to particular entities. This is an imperative question, since people are typically interested in sentiments on specific entities and not in the overall sentiment articulated in an article or a document. Sentiment relevance is aimed at addressing this precise problem. In this paper, we argue that exploiting...
The continuous sophistication in clinical informationprocessing motivates the development of a dictionary likeWordNet for Medical Events in order to convey the valuableinformation (e.g., event definition, sense based contextualdescription, polarity etc.) to the experts (e.g. medicalpractitioners) and non-experts (e.g. patients) in their respective fields. The present paper reports the enrichment of...
In airline service industry, it is difficult to collect data about customers' feedback by questionnaires, but Twitter provides a sound data source for them to do customer sentiment analysis. However, little research has been done in the domain of Twitter sentiment classification about airline services. In this paper, an ensemble sentiment classification strategy was applied based on Majority Vote...
In this work we present a Conversation Classifierbased on Multiple Classifiers, to detect Life Events on SocialMedia. In one hand, conversations can provide more contextand help disambiguate life event detection, compared with single posts. On the other hand, the increase in number of messages and the way they interact with each other within the conversation cannot be trivially modeled by a classifier...
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