Consumer reviews are important information that reflects the quality of E-commerce goods and services and their existing problems after shopping. Due to the possible differences in consumers’ experiences with goods and service quality, consumer reviews can involve multiple-aspect expressions of emotions or opinions. This may result in attitudes expressed by a consumer in the same review sometimes having a variety of emotions. We introduce a sentiment multiclassification method based on a directed weighted model. The model represents the sentiment entity vocabulary as the sentiment nodes and represents the relation between nodes as the directed weighted link. The sentiment entity vocabulary is the entity with attributes, which can express sentiment meaning in related reviews. Directed weighted links represent the sentiment similarity between two nodes of entities with attributes and determined by the direct correlation calculation between them. The paths are all connected directed links from one node to another, which are composed of several nodes and links with close sentiment similarity. Then, we can establish a directed weighted model concerning the sentiments. Directed weighted links having similar sentiment relations with each other may constitute a directed weighted path. There are several directed weighted paths from a start node to the end nodes of the sentiment entity vocabulary in the directed weighted model. Each different path is a different sentiment expression, which represents a different sentiment type. The different sentiment classifications can be obtained through the restriction of path length. Experiments and analysis of the results show that the sentiment multiclassification model based on the directed weighted model proposed in this paper can classify the review sentiments according to different limited threshold rules. Comprehensive analysis indicates the classification results have good accuracy and high efficiency.

The Social Internet of Things (SIoT) is a novel network that integrates social relations among objects and facilitates the interconnection between humans and smart devices through the Internet of Things (IoT). However, with the growing number of users and their devices in SIoT, ensuring high quality of service (QoS) and trust relations among users poses significant challenges. Therefore, this paper proposes a SIoT services recommendation model based on trust and QoS. Our model combines user trust relations and QoS prediction, including the availability, reliability, and efficiency of services. To achieve this, we propose a three-layer services recommendation model consisting of a social network layer, a devices layer, and a services layer. We suggest using direct trust and joint trust to accurately calculate users' trust relations. Additionally, we introduce an Levenberg-Marquardt (L-M) -based algorithm for social network users' trust community clustering and a Random Service System (RSS) -based algorithm for QoS prediction. Experimental results show that our proposed model effectively recommends services in SIoT that exhibit high trustworthiness and quality.

The innovative development of logistics has become a powerful starting point and strong support for the construction of smart cities. An accurate evaluation of logistics service quality can promote intelligent transformation, and upgrading logistics enterprises can improve the urban public service infrastructure. In this study, we propose a logistics service quality evaluation model based on a combination of sentiment analysis technology and a traditional index evaluation system. With the help of sentiment analysis technology, the model focuses on extracting the sentiment characteristics of logistics service quality from user reviews and analyzing user attitudes from different aspects. We designed a new logistics service quality evaluation index system by improving the SERVQUAL model. The system uses sentiment analysis technology to explore evaluation content through feature extraction and builds relations between the evaluation content and indices. Additionally, we use sentiment orientation analysis with different indices to comprehensively evaluate service quality. The experimental analysis shows that the proposed model and algorithm have high accuracy.

As a world-famous and well-developed e-commerce region, the development of e-commerce in Zhejiang province has always attracted people’s wide attention. Based on publicly available e-commerce transaction-related data, basic geographic data, and regional economic and social development data, we use the Gini coefficient to measure the imbalance of e-commerce development in Zhejiang province during 2017–2019. With the help of spatial analyst tools in ArcGIS desktop, the cluster and outlier analysis method is used to study the spatial pattern of e-commerce development in the province at the district or county-level city scale. To explore the causes of spatial aggregation and imbalance of e-commerce in Zhejiang province quantitatively, the paper proposes a geographical weighted regression (GWR) model with 15 economic and social development-related indicators. GWR and ordinary least squares (OLS) analysis indicate that 5 of the 15 selected indicators are highly related to the development of regional e-commerce development in Zhejiang, China.