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Rural social governance is an important part of national governance, facing many difficulties, such as population outflow, resource scarcity, environmental pollution, and lack of rights. To address these challenges, comprehensive measures need to be taken, including strengthening the construction of grassroots organizations, increasing farmers' participation, increasing talent cultivation, and developing information technology. These measures help to improve the governance level of rural society and promote the stability and development of rural society.

When shopping on line, reviewing product reviews often plays a very important role in making decisions for consumers. We will build models, study the relationship between user reviews and other metrics, and propose appropriate sales strategies for the company. First of all, we need existing data for processing. We analyzed the star rating, the voting status of reviews, and the sales volume, and divided the reviews into three levels according to the length of the content of the paper. Finally, we reached the following conclusions: 1) The pacifier has the highest average star rating among the three types of products, and the highest sales volume; 2) Three categories of products have the largest percentage of Five Star reviews, and Microwave for two products, one star rating accounted for a relatively large proportion. Secondly, the TF-IDF algorithm is used to extract keywords from user reviews, and combined with sentiment analysis, an emotional score is assigned to each review. Then, we establish a fuzzy evaluation model and use the reviews that have been scored to analyze the satisfaction of the product. In the end, we got a comprehensive evaluation of the three products, which were all expressed as satisfactory, and the satisfaction degree of the hair dryer was higher than that of the microwave oven and the pacifier. Using the established model and the obtained data, we can find out the trend of star ratings over time, and filter out the most successful products and the most failed products. In particular, for the most failed products, we can describe keywords based on specific quality descriptions in user reviews, and find the shortcomings of the products to improve them. In addition, we built a model and found that after deleting a specific star rating, other users will be affected when they comment. Finally, we screened out specific keywords of five stars and one star respectively. With reference to their emotional scores, we can find that there is a high positiv

Streets are conduits of human activity. Despite their importance, studying street activity has been obscured by a lack of data on how people use them, with most approaches limited to studying a single point in time or small geographic areas. This paper proposes a new framework to measure street activity in real-time. Our framework leverages machine learning and computer vision to classify pedestrian activities and transportation modes using images collected from moving vehicles. We apply our methodology to measure street activity in Paris for five weeks. We produce activity maps for this period showing that streets vary dramatically in their capacity to support pedestrian activity and that these differences are highly persistent. Our proposed framework can be used to measure street activities in other contexts and cities, providing urban researchers with an approach to guide planning interventions, identify infrastructural deficiencies, and inform design policies that foster active streets.

As the living tissue connecting urban places, streets play significant roles in driving city development, providing essential access, and promoting human interactions. Understanding street activities and how these activities vary across different streets is critical for designing both efficient and livable streets. However, current street classification frameworks primarily focus on either streets’ functions in transportation networks or their adjacent land uses rather than actual activity patterns, resulting in coarse classifications. This research proposes an activity-based street classification framework to categorize street segments based on their temporal street activity patterns, which is derived from high-resolution de-identified and privacy-enhanced mobility data. We then apply the proposed framework to 18,023 street segments in the City of Boston and reveal 10 distinct activity-based street types (ASTs). These ASTs highlight dynamic street activities on streets, which complements existing street classification frameworks, which focus on the static or transportation characteristics of the street segments. Our results show that a street classification framework based on temporal street activity patterns can identify street categories at a finer granularity than current methods, which can offer useful implications for state-of-the-art urban management and planning. In particular, we find that our classification distinguishes better those streets where crime is more prevalent than current functional or contextual classifications of streets.