Analysis of Public Big Data Management under Text Analysis

Abstract

Based on text analysis, public big data management is studied. The public data management of Mount Wutai tourism network travel notes is discussed. The positive, neutral, and negative effects of the naive Bayesian classification model and decision tree classification model on the tourism sentiment attitude of Mount Wutai are compared. The relationship between tourism resources, tourism facilities, tourism services, tourism environment, and tourism sentiment and attitude of Wutai Mountain is analyzed. The results show that the true positive rate, true negative rate, and F-measure of the Bayesian decision tree classifier to classify positive text are 86.64%, 81.27%, and 84.62%, respectively. The true positive rate for neutral text is 82.05%, the true negative rate is 78.89%, and the F-measure is 77.11%. The true positive rate for negative text is 83.67%, the true negative rate is 98.29%, and the F-measure is 82.83%. The Bayesian decision tree classifier can evaluate positive and negative texts better than neutral texts. The true positive rate of the C4.5 decision tree classifier for positive text is 91.44%, the true negative rate is 86.57%, and the F-measure is 89.45%. The true positive rate for neutral text is 90.17%, the true negative rate is 83.28%, and the F-measure is 84.06%. The true positive rate for negative text is 91.84%, the true negative rate is 99.05%, and the F-measure is 90.91%. The decision tree classifier has a better evaluation effect on positive and negative texts than on neutral texts. The ROC curve of the evaluation effect of the two classifiers shows that the evaluation effect of the two classifiers has a better evaluation effect on positive text than that of the neutral and negative texts, and the evaluation effect of the C4.5 decision tree classifier is better than that of the Bayesian classifier. The promotion degree of tourism resources and facilities in forwarding online travel notes is obviously higher, and there is a high correlation between tourism resources and facilities and forward online travel notes. In negative online travel notes, the promotion degree of tourism service and tourism environment is high, and the correlation between tourism service and tourism environment and negative online travel notes is high. In summary, improving the quality of tourism services and the tourism environment of Mount Wutai scenic spots can better enhance the recognition and satisfaction of tourists with Mount Wutai tourism.

Application of Improved DEA Algorithm in Public Management Problem Classification

“…The DEA is a great way to learn about the human resources and potential of coal production in China. Some scholars use the DEA method to determine that “finance is the most important factor affecting the performance of China's high-tech industry and human technology” [ 9 ]. He hoped that China's high-tech industry will continue to benefit from the application of science and technology of human resources in the field of performance measurement, innovation baking, and financial management.…”

Dynamic probability integration for electroencephalography-based rapid serial visual presentation performance enhancement: Application in nighttime vehicle detection

“…Posterior probabilities were estimated by each heterogeneous information source, representing the possibilities of targets and non-targets. The true positive rate (TPR) and true negative rate (TNR) combined with the posterior probabilities provided evidence for the three hypotheses (target, non-target and uncertain) (Zhu and Kan, 2022). Then, evidence was fused using the D-S combination rule.…”

“…The TPR and TNR represent the detection capabilities of targets and non-targets, respectively. In addition, the false positive rate (FPR) is another performance metric that refers to the probability of falsely identifying the non-target as the target (Zhu and Kan, 2022). The above indicators are calculated from the confusion matrix, and an illustration of the confusion matrix is shown in Figure 4.…”

Sentiment Classification of Chinese Tourism Reviews Based on ERNIE-Gram+GCN

“…The experimental results showed that the tree long short-term memory network (LSTM) model had a higher F1 value than Bi LSTM in sentiment classification by 1.19% and had a higher classification ability for tourism reviews [ 4 ]. Zhu compared the positive, neutral, and negative effects of the plain Bayesian classification model and the decision tree classification model on the emotional attitude toward tourism in Wutai Mountain [ 5 ]. The experimental results showed that good results were achieved on a self-built dataset for the evaluation of tourist attractions in Wutai Mountain.…”