Hyper-dense wireless network deployment is one of the popular solutions to meeting high capacity requirement for 5G delivery. However, current operator understanding of consumer satisfaction comes from call centers and base station quality-of-service (QoS) reports with poor geographic accuracy. The dramatic increase in geo-tagged social media posts adds a new potential to understand consumer satisfaction towards target-specific quality-of-experience (QoE) topics. In our paper, we focus on evaluating users' opinion on wireless service-related topics by applying natural language processing (NLP) to geo-tagged Twitter data. Current generalized sentiment detection methods with generalized NLP corpora are not topic specific. Here, we develop a novel wireless service topic-specific sentiment framework, yielding higher targeting accuracy than generalized NLP frameworks. To do so, we first annotate a new sentiment corpus called SignalSentiWord (SSW) and compare its performance with two other popular corpus libraries, AFINN and SentiWordNet. We then apply three established machine learning methods, namely: Naïve Bayes (NB), Support Vector Machine (SVM), and Recurrent Neural Network (RNN) to build our topic-specific sentiment classifier. Furthermore, we discuss the capability of SSW to filter noisy and high-frequency irrelevant words to improve the performance of machine learning algorithms. Finally, the real-world testing results show that our proposed SSW improves the performance of NLP significantly.INDEX TERMS Wireless, quality of experience, natural language processing, social media data, consumer.
Objective. The aim of this work was to assess vascular remodeling after the placement of an endovascular neural interface in the superior sagittal sinus of sheep. We also assessed the efficacy of neural recording using an endovascular neural interface. Approach. The study used histological analysis to assess the composition of the foreign body response. Micro-CT images were analyzed to assess the profiles of the foreign body response and create a model of a blood vessel. Computational fluid dynamic modeling was performed on a reconstructed blood vessel to evaluate the blood flow within the vessel. Recording of brain activity in sheep was used to evaluate efficacy of neural recordings. Main Results. Histological analysis showed accumulated extracellular matrix material in and around the implanted endovascular neural interface. The extracellular matrix contained numerous macrophages, foreign body giant cells, and new vascular channels lined by endothelium. Image analysis of CT slices demonstrated an uneven narrowing of the superior sagittal sinus lumen proportional to the stent material within the blood vessel. However, the foreign body response did not occlude blood flow. The endovascular neural interface was able to record epileptiform spiking activity with distinct spike morphologies. Significance. This is the first study to show high-resolution tissue profiles, the histological response to an implanted endovascular neural interface and blood flow dynamic modeling based on blood vessels implanted with an endovascular neural interface. The results from this study can be used to guide surgical planning and future endovascular neural interface designs; stent oversizing parameters to blood vessel diameter should be considered to minimize detrimental vascular remodeling.
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