Research on Sentiment Analysis of Network Forum Based on BP Neural Network

Tang, Yiming; Su, Jianhuan; Khan, Muazzam A.

doi:10.1007/s11036-020-01697-y

Cited by 11 publications

(6 citation statements)

References 21 publications

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“…In addition, the index weights are derived by the entropy weight method, which is based on the idea that the greater the entropy of information, the more information there is, and vice versa. e entropy weight method is calculated by the formula [26]:…”

Section: Data Preprocessingmentioning

confidence: 99%

A Dynamic Econometric Analysis of Urbanization and Ecological Environment in Silk Road Economic Belt

Luo

2022

Scientific Programming

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On the basis of the construction of urbanization and ecology index system, this study takes the core area of Silk Road Economic Belt, Gansu, as the research object and creates the coordination evaluation model between urbanization and ecological environment in Gansu; meanwhile, it uses BP neural network to predict the coordinated development of urbanization and ecology in Gansu Province. Finally, the aforesaid model is validated by means of empirical evidence. The results show that Gansu, in the Silk Road Economic Belt, has accelerated its urbanization level in the 10-year period from 2010 to 2019 in the context of the Belt and Road Initiative, especially in Jiayuguan and Lanzhou, mainly due to its industrialization-led urbanization； meanwhile, in terms of coupling degree, most of them are between 0.45 and 0.55, and most of them are out-of-frequency pro-disorder recession type, indicating that under the background of urbanization, the ecology of each city and prefecture in Gansu is also under great pressure. At the same time, through the forecast, Gansu is considered to be in the red light in 2019, as the endangered disorder recession B environmental lagging type, indicating that Lanzhou is facing great ecological pressure, but in general, the relationship between human and land is gradually developing in harmony.

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Section: Data Preprocessingmentioning

confidence: 99%

A Dynamic Econometric Analysis of Urbanization and Ecological Environment in Silk Road Economic Belt

Luo

2022

Scientific Programming

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“…The input sample vector is processed by the input layer and the hidden layer's neurons, as shown in Equation ( 6), and is outputted by the output layer's neurons, as shown in Equation (7). The learning process of BP is mainly through weight adjustment, and a BP neural network has a strong self-learning capacity plus the ability to adapt [43,44].…”

Section: Bp Modelmentioning

confidence: 99%

A Study of a Model for Predicting Pneumatic Subsoiling Resistance Based on Machine Learning Techniques

et al. 2023

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In order to explore the drag reduction mechanism of pneumatic subsoiling and study the influence of pneumatic subsoiling on the soil, this study used machine learning models to predict the working resistance of a pneumatic subsoiler and adopted random forest (RF), error back-propagation (BP), eXtreme gradient boosting (XGBoost) and support vector regression (SVR) to analyze and compare the predictions of these four models. Field experiments were carried out in two fields with different bulk densities and moisture content. The effects of these parameters on the resistance of pneumatic subsoiling were studied by changing the working air pressure, depth and forward speed. In the RF, SVR, XGBoost and BP models, five parameters (working air pressure, working depth, forward speed, bulk density and moisture content) were inputted as independent variables, and the operating resistance of pneumatic subsoiling was used as the predicted value. After training the four models, the results showed that the R2 value of the RF model was the highest and the error was the smallest, which made it better than the SVR, XGBoost and BP models. The values of MAPE, R2 and RMSE for the RF model’s test set were 0.01, 0.99, and 3.61 N, respectively, indicating that the RF model could predict the resistance value of subsoiling well. When the RF model was used to analyze the five input parameters, the experimental results showed that the contribution of working air pressure to reducing the resistance of subsoiling reached 29%, indicating that pneumatic subsoiling can reduce the resistance, drag and consumption.

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“…( 3)). The process of weight adjustment is the process of network learning (Tang et al 2020). BP neural network has strong self-learning and self-adapting abilities (Peng et al 2020), a threelayer BP neural network can simulate any complex nonlinear problem.…”

Section: Bp Neural Networkmentioning

confidence: 99%

Short-term Runoff Prediction Optimization Method Based on BGRU-BP and BLSTM-BP Neural Networks

Sang

Yin

et al. 2022

Water Resour Manage

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Runoff forecasting is one of the important non-engineering measures for flood prevention and disaster reduction. The accurate and reliable runoff forecasting mainly depends on the development of science and technology, many machine learning models have been proposed for runoff forecasting in recent years. Considering the non-linearity and real-time of hourly rainfall and runoff data. In this study, two runoff forecasting models were proposed, which were the combination of the bidirectional gated recurrent unit and backpropagation (BGRU-BP) neural network and the bidirectional long short-term memory and backpropagation (BLSTM-BP) neural network. The two models were compared with the gated recurrent unit (GRU), long short-term memory (LSTM), bidirectional gated recurrent unit (BGRU), and bidirectional long short-term memory (BLSTM) models. The research methods were applied to simulate runoff in the Yanglou hydrological station, Northern Anhui Province, China. The results show that the bidirectional models were superior to the unidirectional model, and the backpropagation (BP) based bidirectional models were superior to the bidirectional models. The bidirectional propagation was conducive to improving the generalization ability of the model, and BP neural network could better guide the model to find the optimal nonlinear relationship. The results also show that the BGRU-BP model performs equally well as the BLSTM-BP model. The BGRU-BP model has few parameters and a short training time, so it may be the preferred method for short-term runoff forecasting.

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Research on Sentiment Analysis of Network Forum Based on BP Neural Network

Cited by 11 publications

References 21 publications

A Dynamic Econometric Analysis of Urbanization and Ecological Environment in Silk Road Economic Belt

A Dynamic Econometric Analysis of Urbanization and Ecological Environment in Silk Road Economic Belt

A Study of a Model for Predicting Pneumatic Subsoiling Resistance Based on Machine Learning Techniques

Short-term Runoff Prediction Optimization Method Based on BGRU-BP and BLSTM-BP Neural Networks

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