Research on Online Word-of-mouth Sentiment Analysis and Attribute Extraction Based on Deep Learning

Online word-of-mouth content mining is of great significance to product, service improvement and demand prediction of online marketing enterprises. However, most studies have focused on the identification of the sentiment tendency of online word-of-mouth, and lack of text content mining for online word-of-mouth, especially negative word-of-mouth. This paper introduces deep learning into online word-of-mouth sentiment tendency analysis and negative word-of-mouth word attribute feature extraction, and builds an online word-of-mouth sentiment tendency analysis and attribute extraction model based on LSTM deep learning algorithm. The model was trained and tested through online word-of-mouth data of a fashion apparel e-commerce company. The results show that the LSTM model has a good effect on sentiment analysis and negative word-of-mouth attribute feature extraction. Through comparative experiments, it is shown that the model has a better effect than the traditional machine learning methods (SVM, Naive Bayes) in the analysis of sentiment tendency

SVM-Based Spectrum Mobility Prediction Scheme in Mobile Cognitive Radio Networks

Spectrum mobility as an essential issue has not been fully investigated in mobile cognitive radio networks (CRNs). In this paper, a novel support vector machine based spectrum mobility prediction (SVM-SMP) scheme is presented considering time-varying and space-varying characteristics simultaneously in mobile CRNs. The mobility of cognitive users (CUs) and the working activities of primary users (PUs) are analyzed in theory. And a joint feature vector extraction (JFVE) method is proposed based on the theoretical analysis. Then spectrum mobility prediction is executed through the classification of SVM with a fast convergence speed. Numerical results validate that SVM-SMP gains better short-time prediction accuracy rate and miss prediction rate performance than the two algorithms just depending on the location and speed information. Additionally, a rational parameter design can remedy the prediction performance degradation caused by high speed SUs with strong randomness movements

Bipolar ice-core records constrain possible dates and global radiative forcing following the ∼74 ka Toba eruption

The Younger Toba Tuff eruption ∼74 ka ago in Indonesia, is among the largest known supereruptions in the Quaternary and its potential impact on the climate system and human evolution remains controversially debated. The eruption is dated radiometrically to 73.88 ± 0.32 ka (1σ, Storey et al., 2012) and it occurred at the abrupt cooling transition from Greenland Interstadial 20 to Greenland Stadial 20. The precise stratigraphic position of volcanic fallout detected in ice cores from both polar ice sheets has previously been narrowed down to four potential candidates. Here, we compile all available Greenland and Antarctic sulfate records, together with electrical conductivity records and recently obtained sulfur isotope records to identify, quantify and characterize these Toba candidates in terms of their likely latitudinal position of eruption, sulfur emission strength and radiative forcing. We identify that the youngest event of the four candidates is composed of two separate eruptions, both likely located in the extra-tropical Northern Hemisphere. We deem the two older events unlikely candidates for the Toba eruption because of their limited sulfur emission strengths. The second youngest event has the largest sulfur output of the Toba candidates, and it is also larger than any other volcanic event identified in ice core records over the last 60 kyr. Comparable amounts of sulfate deposits in Greenland and Antarctica strongly suggest a tropical source. We thus propose the second youngest event (74,156 years before 2000 CE) to be most likely associated with the Toba eruption. The estimated stratospheric sulfate loading of the proposed Toba eruption is 535 ± 96 Tg, which is 3 times that of Samalas 1258 CE, 6 times that of Tambora (1815) CE and 20 times that of Pinatubo (1991) CE. We derive the continuous time-series of volcanic sulfate deposition, sulfur emission strength and radiative forcing over the 74.8–73.8 ka time window, suitable for conducting experiments with climate models that either require prescribed forcing field or interactively reproduce aerosol processes. We estimate the cumulative volcanic sulfur emission strength and the radiative forcing of the two younger events and they are found to be much stronger than those at the onset of the Younger Dryas and those preceding the Little Ice Age. Stacked Greenland water isotope records show an accelerated transition trend and abrupt shift after the proposed Toba eruption and suggest that the Greenland moisture source moved southward shortly after the Toba eruption. The Toba eruption may thus have an amplifying effect on the cooling transition leading to Greenland Stadial 20

Baseline of Pollution of Heavy Metals and Physico-chemical Parameters in Surface Sediments from Quanzhou Bay, China, in 2006-2007

According to the monitoring results of the near-shore sediments of Quanzhou Bay in 2006-2007, we analyzed the near-shore depositional environmental quality of Quanzhou Bay and assessed it by the single-factor evaluation on the basis of corresponding standards of local marine functional areas. The results showed that the sediments from the inner part of Quanzhou Bay were polluted more seriously than that of the open part, which might be due to the increasing human activities in coastal areas. The main exceeding standard items are petroleum, Cu, Zn and Pb. In additions, the pollutions caused by sulfide and Cr are different in different regions. The contents of Hg and As are basically in according with the sedimentary quality standards of the corresponding marine functional areas. According the evaluating results, we also provided the corresponding measures to control the pollutions in sedimentary environment of Quanzhou Bay. (C) 2011 Published by Elsevier B.V. Selection and/or peer-review under responsibility of National University of Singapore

Assessing the spatial heterogeneity of tuberculosis in a population with internal migration in China: a retrospective population-based study

BackgroundInternal migrants pose a critical threat to eliminating Tuberculosis (TB) in many high-burden countries. Understanding the influential pattern of the internal migrant population in the incidence of tuberculosis is crucial for controlling and preventing the disease. We used epidemiological and spatial data to analyze the spatial distribution of tuberculosis and identify potential risk factors for spatial heterogeneity.MethodsWe conducted a population-based, retrospective study and identified all incident bacterially-positive TB cases between January 1st, 2009, and December 31st, 2016, in Shanghai, China. We used Getis-Ord Gi* statistics and spatial relative risk methods to explore spatial heterogeneity and identify regions with spatial clusters of TB cases, and then used logistic regression method to estimate individual-level risk factors for notified migrant TB and spatial clusters. A hierarchical Bayesian spatial model was used to identify the attributable location-specific factors.ResultsOverall, 27,383 bacterially-positive tuberculosis patients were notified for analysis, with 42.54% (11,649) of them being migrants. The age-adjusted notification rate of TB among migrants was much higher than among residents. Migrants (aOR, 1.85; 95%CI, 1.65-2.08) and active screening (aOR, 3.13; 95%CI, 2.60-3.77) contributed significantly to the formation of TB high-spatial clusters. With the hierarchical Bayesian modeling, the presence of industrial parks (RR, 1.420; 95%CI, 1.023-1.974) and migrants (RR, 1.121; 95%CI, 1.007-1.247) were the risk factors for increased TB disease at the county level.ConclusionWe identified a significant spatial heterogeneity of tuberculosis in Shanghai, one of the typical megacities with massive migration. Internal migrants play an essential role in the disease burden and the spatial heterogeneity of TB in urban settings. Optimized disease control and prevention strategies, including targeted interventions based on the current epidemiological heterogeneity, warrant further evaluation to fuel the TB eradication process in urban China

Identification and characterization of a 25-lncRNA prognostic signature for early recurrence in hepatocellular carcinoma

Background Early recurrence is the major cause of poor prognosis in hepatocellular carcinoma (HCC). Long non-coding RNAs (lncRNAs) are deeply involved in HCC prognosis. In this study, we aimed to establish a prognostic lncRNA signature for HCC early recurrence. Methods The lncRNA expression profile and corresponding clinical data were retrieved from total 299 HCC patients in TCGA database. LncRNA candidates correlated to early recurrence were selected by differentially expressed gene (DEG), univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. A 25-lncRNA prognostic signature was constructed according to receiver operating characteristic curve (ROC). Kaplan-Meier and multivariate Cox regression analyses were used to evaluate the performance of this signature. ROC and nomogram were used to evaluate the integrated models based on this signature with other independent clinical risk factors. Gene set enrichment analysis (GSEA) was used to reveal enriched gene sets in the high-risk group. Tumor infiltrating lymphocytes (TILs) levels were analyzed with single sample Gene Set Enrichment Analysis (ssGSEA). Immune therapy response prediction was performed with TIDE and SubMap. Chemotherapeutic response prediction was conducted by using Genomics of Drug Sensitivity in Cancer (GDSC) pharmacogenomics database. Results Compared to low-risk group, patients in high-risk group showed reduced disease-free survival (DFS) in the training (p < 0.0001) and validation cohort (p = 0.0132). The 25-lncRNA signature, AFP, TNM and vascular invasion could serve as independent risk factors for HCC early recurrence. Among them, the 25-lncRNA signature had the best predictive performance, and combination of those four risk factors further improves the prognostic potential. Moreover, GSEA showed significant enrichment of “E2F TARGETS”, “G2M CHECKPOINT”, “MYC TARGETS V1” and “DNA REPAIR” pathways in the high-risk group. In addition, increased TILs were observed in the low-risk group compared to the high-risk group. The 25-lncRNA signature negatively associates with the levels of some types of antitumor immune cells. Immunotherapies and chemotherapies prediction revealed differential responses to PD-1 inhibitor and several chemotherapeutic drugs in the low- and high-risk group. Conclusions Our study proposed a 25-lncRNA prognostic signature for predicting HCC early recurrence, which may guide postoperative treatment and recurrence surveillance in HCC patients