Summary With the advanced development of smart devices and network technique, Internet of Things has seen a large number of popular applications, among which, smart agriculture is a good example. The sensor nodes collect some parameters in the greenhouse, and send them to the control center. Then the control center can conduct some operations according to the analysis of the collected parameters. In this paper, we discuss how to efficiently aggregate and collect data with features of privacy protection in smart agricultural system. We propose an effective and scalable framework. The genetic algorithm is used to obtain the optimized data collection route for the agricultural system. The use of unmanned aerial vehicle also greatly improves the communication efficiency of resource‐constrained sensors in the system, which further increases the use time of the entire agricultural system. The experimental analysis shows that our framework has good efficiency and enjoys good scalability.

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Summary At present, there are many group incidents on the Internet, which has caused a high level of public opinion. Hot issues on the Internet can often trigger intense discussions among netizens and finally evolve toward bipolarization or multipolarization. This article focuses on the phenomenon of public opinion polarization under the dynamic network. First, the single‐dimensional evaluation system for the JA model was expanded to multidimensional and analyzes the main factors that cause the polarization of public opinion of network groups from multiple dimensions of hot events. Second, the strength of the relationship between individuals and its dynamic changes were taken into account in the model. The strength of the relationship between individuals and its changes will affect the interaction of opinions, and thus affect the degree and speed of group polarization. Third, combining the changing process of the strength of relationship, the dynamic connection mechanism of network nodes was set to make the network structure more consistent with the social network in real life. Finally, we verify the reliability and scientificity of the model by combining practical cases.

Human logical thinking is in the form of natural language. With the development of computer science technique, it becomes easier and more convenient for natural language processing. Therefore, a variety number of natural language processing applications have emerged. Sentiment analysis is one of these novel applications, and has been applied in many areas. In Amazon.com, there are a large number of user comments and product discussions, which can help a person to decide whether to buy a product or not without asking the opinions from friends and family members. Therefore, sentiment analysis on user comments and product discussions, such as Amazon review becomes increasingly useful and important. In this paper, the effect of corpus on sentiment analysis of Amazon review dataset with the aid of support vector machine are studied. We generate eight different size datasets from Amazon review dataset filtered by different word frequency in Corpus of Contemporary American, and conduct some experiments on these eight datasets. According to the experimental result, we make some conclusion and give some suggestions to facilitate researchers to make a trade-off between accuracy and experimental cost.

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