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Aspect category-based sentiment analysis (ACSA) has been widely used in consumer preference mining and marketing strategy formulation. However, existing studies ignore the variability in features and the intrinsic correlation among diverse aspect categories in ACSA tasks. To address these problems, this paper aims to propose a novel integrated framework.

The integrated framework consists of three modules: text feature extraction and fusion, adaptive feature selection and category-aware decision fusion. First, text features from global and local views are extracted and fused to comprehensively capture the potential information in the different dimensions of the review text. Then, an adaptive feature selection strategy is devised for each aspect category to determine the optimal feature set. Finally, considering the intrinsic associations between aspect categories, a category-aware decision fusion strategy is constructed to enhance the performance of ACSA tasks.

Comparative experimental results demonstrate that the integrated framework can effectively detect aspect categories and their corresponding sentiment polarities from review texts, achieving a macroaveraged F1 score (Fmacro) of 72.38% and a weighted F1 score (F1) of 79.39%, with absolute gains of 2.93% to 27.36% and 4.35% to 20.36%, respectively, compared to the baselines.

This framework can simultaneously detect aspect categories and corresponding sentiment polarities from review texts, thereby assisting e-commerce enterprises in gaining insights into consumer preferences, prioritizing product improvements, and adjusting marketing strategies.

Twitter fake accounts refer to bot accounts created by third-party organizations to influence public opinion, commercial propaganda or impersonate others. The effective identification of bot accounts is conducive to accurately judge the disseminated information for the public. However, in actual fake account identification, it is expensive and inefficient to manually label Twitter accounts, and the labeled data are usually unbalanced in classes. To this end, the authors propose a novel framework to solve these problems.

In the proposed framework, the authors introduce the concept of semi-supervised self-training learning and apply it to the real Twitter account data set from Kaggle. Specifically, the authors first train the classifier in the initial small amount of labeled account data, then use the trained classifier to automatically label large-scale unlabeled account data. Next, iteratively select high confidence instances from unlabeled data to expand the labeled data. Finally, an expanded Twitter account training set is obtained. It is worth mentioning that the resampling technique is integrated into the self-training process, and the data class is balanced at the initial stage of the self-training iteration.

The proposed framework effectively improves labeling efficiency and reduces the influence of class imbalance. It shows excellent identification results on 6 different base classifiers, especially for the initial small-scale labeled Twitter accounts.

This paper provides novel insights in identifying Twitter fake accounts. First, the authors take the lead in introducing a self-training method to automatically label Twitter accounts from the semi-supervised background. Second, the resampling technique is integrated into the self-training process to effectively reduce the influence of class imbalance on the identification effect.

The outbreak of COVID-19 has become a major public health emergency worldwide. How to effectively guide public opinion and implement precise prevention and control is a hot topic in current research. Mining the spatiotemporal coupling between online public opinion and offline epidemics can provide decision support for the precise management and control of future emergencies.

This study focuses on analyzing the spatiotemporal coupling relationship between public opinion and the epidemic. First, based on Weibo information and confirmed case information, a field framework is constructed using field theory. Second, SnowNLP is used for sentiment mining and LDA is utilized for topic extraction to analyze the topic evolution and the sentiment evolution of public opinion in each coupling stage. Finally, the spatial model is used to explore the coupling relationship between public opinion and the epidemic in space.

The findings show that there is a certain coupling between online public opinion sentiment and offline epidemics, with a significant coupling relationship in the time dimension, while there is no remarkable coupling relationship in space. In addition, the core topics of public concern are different at different coupling stages.

This study deeply explores the spatiotemporal coupling relationship between online public opinion and offline epidemics, adding a new research perspective to related research. The result can help the government and relevant departments understand the dynamic development of epidemic events and achieve precise control while mastering the dynamics of online public opinion.

This paper aims to investigate how online consumer reviews (OCRs), countdowns and self-control affect consumers' online impulse buying behavior in online group buying (OGB) and uncover the relationship between these factors.

Based on the stimulus-organism-response (SOR) framework, this research examines the effects of OCRs, countdowns and self-control on users' impulse purchases. First, the influence of emotions on impulse purchases in group purchasing is investigated. In addition, this study innovatively applies stress-coping theory to group buying research, with countdowns exerting temporal pressure on consumers and OCRs viewed as social pressure, to investigate in depth how countdowns and OCRs affect users' impulse purchase behavior. Finally, this study also surveys the moderating role of users' self-control in the impulse purchase process.

The results show that the perceived value of OCRs and positive emotions (PE) were positively correlated with impulsiveness (IMP) and the urge to buy impulsively (UBI), while negative emotions (NE) were negatively correlated with IMP. Countdowns (CD) had a positive effect on UBI. Self-control can indirectly affect users' impulse buying by negatively moderating the relationship between PE and UBI, PE and IMP and CD and UBI.

The research results can help group buying platforms and related participants understand the factors influencing users' impulse purchases in OGB and facilitate them to better design strategies to increase product sales.

The deployment of vaccines is the primary task in curbing the COVID-19 pandemic. The purpose of this paper is to understand the public’s opinions on vaccines and then design effective interventions to promote vaccination coverage.

This paper proposes a research framework based on the spatiotemporal perspective to analyse the public opinion evolution towards COVID-19 vaccine in China. The framework first obtains data through crawler tools. Then, with the help of data mining technologies, such as emotion computing and topic extraction, the evolution characteristics of discussion volume, emotions and topics are explored from spatiotemporal perspectives.

In the temporal perspective, the public emotion declines in the later stage, but overall emotion performance is positive and stabilizing. This decline in emotion is mainly associated with ambiguous information about the COVID-19 vaccine. The research progress of vaccines and the schedule of vaccination have driven the evolution of public discussion topics. In the spatial perspective, the public emotion tends to be positive in 31 regions, whereas local emotion increases and decreases in different stages. The dissemination of distinctive information and the local epidemic prevention and control status may be potential drivers of topic evolution in local regions.

The analysis results of media information can assist decision-makers to accurately grasp the subjective thoughts and emotional expressions of the public in terms of spatiotemporal perspective and provide decision support for macro-control response strategies and risk communication.

The purpose of this paper is to build a mobile visual search service system for the protection of Dunhuang cultural heritage in the smart library. A novel mobile visual search model for Dunhuang murals is proposed to help users acquire rich knowledge and services conveniently.

First, local and global features of images are extracted, and the visual dictionary is generated by the k-means clustering. Second, the mobile visual search model based on the bag-of-words (BOW) and multiple semantic associations is constructed. Third, the mobile visual search service system of the smart library is designed in the cloud environment. Furthermore, Dunhuang mural images are collected to verify this model.

The findings reveal that the BOW_SIFT_HSV_MSA model has better search performance for Dunhuang mural images when the scale-invariant feature transform (SIFT) and the hue, saturation and value (HSV) are used to extract local and global features of the images. Compared with different methods, this model is the most effective way to search images with the semantic association in the topic, time and space dimensions.

Dunhuang mural image set is a part of the vast resources stored in the smart library, and the fine-grained semantic labels could be applied to meet diverse search needs.

The mobile visual search service system is constructed to provide users with Dunhuang cultural services in the smart library. A novel mobile visual search model based on BOW and multiple semantic associations is proposed. This study can also provide references for the protection and utilization of other cultural heritages.

This study investigates the credit supervision issue in rural e-commerce. By studying the trading strategies of buyers and sellers under different credit supervision measures and the impact of different pricing strategies on the trading strategies of both parties, this paper proposes regulatory suggestions for the increasingly severe credit problems in rural e-commerce.

In the online agricultural product transaction between farmers and consumers, both parties' decision-making is a dynamic process. Using the copying dynamic model of the evolutionary game, this study establishes two evolutionary game models to explore the factors affecting credit supervision in the rural e-commerce transaction process. Then, the study provides corresponding countermeasures and suggestions.

First, credit supervision measures implemented by rural e-commerce platforms and the Government's legal system construction and infrastructure construction guarantees influence both parties' trust choices in rural e-commerce transactions. Second, price is a key factor affecting both parties' trading strategies. In the case of relatively fair prices, the higher the proportion of farmers who choose “low price” and “honest transaction” strategies, the easier that is for consumers to choose to trust farmers. In contrast, the higher the price, the higher the proportion of consumers who choose the “trust farmers” strategy, and the more willing farmers are to choose honest transactions.

This work develops a new approach for analyzing rural e-commerce credit supervision. Moreover, this study helps establish and improve the credit supervision mechanism of rural e-commerce and further realize the long-term sustainable development of the rural economy.

This study aims to make some contribution to the process of corporate compliance governance in China.

This paper adopts qualitative method, literature research, case analysis and comparative methods to explore the Chinese compliance governance model in the field of collusive bidding crimes.

In the process of criminal prosecution of enterprises suspected of committing crimes, the judicial authorities should promote the restoration of normal production and operation of corporate enterprises by promoting the construction of corporate compliance, which is conducive to solving the difficult problem of attribution of collusive bidding crimes. In addition, corporate compliance under prosecutorial supervision is also conducive to optimizing the regulatory path of collusive bidding and achieving more effective prevention and control of unit crimes in the mode of co-regulation between the state and corporate.

Compliance governance corporate crime is at a nascent stage in China, and this study seeks to provide some reference for future compliance review governance in China through the analysis of specific business crime cases.

The purpose of this study is to provide an in-depth understanding about the indoor-orbital electrical inspection robot, which is useful for motivating the further investigation on the inspection of electrical equipment. Currently, electric energy has a strong correlation with the economic development of the country. Intelligent substations play an important role in the transmission and distribution of the electricity; the maintenance of the substation has attracted intensive attention due to the requirement of reliability and safety. The indoor-orbital electrical inspection robot has increasingly become the main tool to realize the unmanned. Hence, a systematic review is conducted systematically reviewing the current technical status of the indoor-orbital electrical inspection robot and discuss the existed problems.

In this paper, the most essential achievements in the field of indoor-orbital electrical inspection robots were reported to present the current status, and the mechanical structures and key inspective technologies were also discussed.

Four recommendations are provided from the analyzed review, which have made constructive comments on the overall structural design, functionality, intelligence and future development direction of the indoor-orbital electrical inspection robot, respectively.

To the best of the authors’ knowledge, this is the first systematic review study on indoor-orbital electrical inspection robots; it fills the theoretical gap and proffers design ideas and directions for the development of the indoor-orbital electrical inspection robot.

Smart tourism is undergoing a trend of rapid development. The quality of service in smart tourism forms the basis of tourists’ evaluations, it needs to be investigated. However, as high-quality smart tourism technologies (STTs) can enhance the overall tourist experience and increase tourist satisfaction, and there is no standard service quality evaluation system for STTs. Therefore, this study aims to explore how the quality of STTs is evaluated from the tourist’s perspective.

In this study, the authors develop a measurement scale for smart tourism technology quality (STTQUAL) based on qualitative interviews, the Delphi method and a survey conducted in three cities that use smart tourism: Macau, Chengdu and Hangzhou.

The STTQUAL scale encompasses 37 measurement items in 7 dimensions: functionality, security, informativity, reliability, responsiveness, convenience and empathy. These dimensions encompass both technical and nontechnical aspects of service.

This study enriches the smart tourism research literature, provides a reference for future research and helps relevant stakeholders understand tourists’ views on STTQUAL. Recommendations are provided to governments, the tourism industry and system developers for how to proceed in future development.

This is a mixed-methods study that fulfills established logical research criteria and proposes a scale for evaluating STTQUAL. The scale is validated through exploratory factor analysis and confirmatory factor analysis.

智慧旅游正经历快速发展的趋势, 其服务质量构成了游客评价的基础, 因此需要进行深入研究。然而, 由于高质量的智慧旅游技术（STTs）可以提升游客的整体体验并提高满意度，目前尚未有针对智慧旅游技术STTs的标准服务质量评价体系。因此，本研究旨在探讨如何从游客的角度理解STTs质量的评价方式。

本研究基于定性访谈、德尔菲法以及在澳门、成都和杭州三座智慧旅游城市进行的问卷调查, 开发了一套智慧旅游技术质量（STTQUAL）测量量表。

STTQUAL量表涵盖了7个维度的37项测量指标, 包括功能性、安全性、信息性、可靠性、响应性、便利性和同理心。这些维度综合了服务的技术和非技术方面。

本研究丰富了智慧旅游研究文献, 为未来研究提供了参考, 并帮助相关利益相关者理解游客对STTQUAL的看法。研究为政府、旅游业和系统开发者在未来发展中的行动提供了建议。

本研究采用混合研究方法, 符合既定的逻辑研究标准, 并提出了一套用于评估STTQUAL的量表。通过探索性因子分析和验证性因子分析对量表进行了验证。