The purpose is to make for the traditional Network Public Opinion (NPO) analysis methods’ inadequacy in the era of big data and provide a sufficient decision-making basis for managers. Based on the Internet of Things (IoT) and big data, this work applies Natural Language Processing (NLP) to NPO analysis. Additionally, it takes the content of Microblog text format as the main collection target, constructs a big data collection tool, and establishes Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Deep Pyramid Convolutional Neural Network (DPCNN) based on Tensorflow and other deep learning models. It is also improved in combination with the characteristics of the model, and a new model is proposed. Finally, the performance of various models is compared and analyzed through experiments, and the path is proposed for the government to use big data to improve the ability to govern NPO and help social governance. The results show that the improved LSTM model can correctly classify the extracted Microblog text’s emotion by as much as 80.00%. It improves the classification accuracy by nearly two percentage points under the ideal condition. Thus, by adding residual connection and attention mechanism, the model can extract the emotional features in the text better and improve the emotional discrimination ability. The public opinion of online media without effective control will have great security risks to social governance under the big data and IoT. The proposed method is of great help in analyzing NPO through the accurate analysis of Microblog text.
The variety of daily household garbage is complex, which is not easy to recycle. On this basis, the methods of household micro garbage classification, recycling treatment and heat energy utilization of unrecyclable garbage are put forward. A kind of household micro garbage processor is designed, which mainly includes garbage sorting module, recyclable processing module and unrecyclable garbage processing module. And experiments on garbage classification recognition were conducted to obtain model training data for the limitations of existing garbage image datasets, using crawling techniques to collect image data from the web, expanding the garbage categories in the dataset and each category of garbage image dataset. To better extract the features of different categories of garbage images, migration learning techniques are used to train the garbage classification model. Since the multi-category problem is addressed in the article, the model evaluation is performed using confusion matrix. The classification efficiency and identification accuracy of micro garbage disposal meet the daily household requirements. The device not only reduces the burden of waste disposal and the environment, but also can make further use of the energy of unrecyclable waste.
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