The basic concept of smart environment is to be aware of the context information related to environmental and human behavioral changes, and to provide appropriate services accordingly. To obtain the context informatio...
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The basic concept of smart environment is to be aware of the context information related to environmental and human behavioral changes, and to provide appropriate services accordingly. To obtain the context informatio...
详细信息
The basic concept of smart environment is to be aware of the context information related to environmental and human behavioral changes, and to provide appropriate services accordingly. To obtain the context information, people often use video cameras, microphones, and other devices. These devices can obtain a wealth of environmental information, but could easily lead to privacy issues. Large environmental information also need more computing resources to process the information. In this paper, we study human behavior identification based on simple sensors. Using fuzzy logic we analyze the sensor data and try to identify four different activities. Experimental results show that the proposed method can correctly identify 83.7 % of these four activities. Results given here will be useful for designing smart homes, smart office, and so on.
This paper focuses on how to improve aspect-level opinion mining for online customer reviews. We first propose a novel generative topic model, the Joint Aspect/Sentiment (JAS) model, to jointly extract aspects and asp...
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This paper focuses on how to improve aspect-level opinion mining for online customer reviews. We first propose a novel generative topic model, the Joint Aspect/Sentiment (JAS) model, to jointly extract aspects and aspect-dependent sentiment lexicons from online customer reviews. An aspect-dependent sentiment lexicon refers to the aspect-specific opinion words along with their aspect-aware sentiment polarities with respect to a specific aspect. We then apply the extracted aspectdependent sentiment lexicons to a series of aspect-level opinion mining tasks, including implicit aspect identification, aspect-based extractive opinion summarization, and aspect-level sentiment classification. Experimental results demonstrate the effectiveness of the JAS model in learning aspectdependent sentiment lexicons and the practical values of the extracted lexicons when applied to these practical tasks.
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