social Media specifically Micro-blogging sites have become very rich data repositories. However the generated data is dynamic by nature, tied to temporal conditions and the subjectivity of its users. Everyday life exp...
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ISBN:
(纸本)9781509021802
social Media specifically Micro-blogging sites have become very rich data repositories. However the generated data is dynamic by nature, tied to temporal conditions and the subjectivity of its users. Everyday life experiences, discussions or events have a direct impact on the behaviors reflected in socialnetworks. It is therefore of great importance to asses the impact these interactions are having over a social group. An alternative to answer this is determining how influential a topic is according to the behavior presented on a social network over time. It is then necessary to find and develop methods that can contribute towards this task. Having identified a topic in social media we can first classify it as time specific or long term, then posts relevant to the topic are collected and each one is assigned an emotion label. We then propose an Influence Value score which will be given to each topic based on its lifespan, emotion transition and reach. This lays ground to quantify how influential a topic is over a social group, specifically from events detected on twitter.
In the age of information explosion, efficiently categorizing the topic of a document can assist our organization and comprehension of the vast amount of text. In this paper, we propose a novel approach, named DKV, fo...
详细信息
ISBN:
(纸本)9781467396073
In the age of information explosion, efficiently categorizing the topic of a document can assist our organization and comprehension of the vast amount of text. In this paper, we propose a novel approach, named DKV, for document categorization using distributed real-valued vector representation of keywords learned from neural networks. Such a representation can project rich context information (or embedding) into the vector space, and subsequently be used to infer similarity measures among words, sentences, and even documents. Using a Chinese news corpus containing over 100,000 articles and five topics, we provide a comprehensive performance evaluation to demonstrate that by exploiting the keyword embeddings, DKV paired with support vector machines can effectively categorize a document into the predefined topics. Results demonstrate that our method can achieve the best performances compared to several other approaches.
A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mits...
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