SUMMARYExisting studies on transportation mode detection from global positioning system (GPS) trajectories mainly adopt handcrafted features. These features require researchers with a professional background and do not always work well because of the complexity of traffic behavior. To address these issues, we propose a model using a sparse autoencoder to extract point-level deep features from point-level handcrafted features. A convolution neural network then aggregates the point-level deep features and generates a trajectory-level deep feature. A deep neural network incorporates the trajectory-level handcrafted features and the trajectory-level deep feature for detecting the users' transportation modes. Experiments conducted on Microsoft's GeoLife data show that our model can automatically extract the effective features and improve the accuracy of transportation mode detection. Compared with the model using only handcrafted features and shallow classifiers, the proposed model increases the maximum accuracy by 6%.
Abstract. This paper presents a language modeling approach to the sentiment detection problem. It captures the subtle information in text processing to character the semantic orientation of documents as "thumb up" (positive) or "thumb down" (negative). To handle this problem, we propose an idea to estimate both the positive and negative language models from training collections. Tests are done through computing the Kullback-Leibler divergence between the language model estimated from test document and these two trained sentiment models. We assert the polarity of a test document by observing whether its language model is close to the trained "thumb up" model or the "thumb down" model. When compared with an outstanding classifier, i.e., SVMs on movie review corpus, language modeling approach showed its better performance.
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