From the end of 2018 in China, the Big-data Driven Price Discrimination (BDPD) of online consumption raised public debate on social media. To study the consumers’ attitude about the BDPD, this study constructed a semantic recognition frame to deconstruct the Affection-Behavior-Cognition (ABC) consumer attitude theory using machine learning models inclusive of the Labeled Latent Dirichlet Allocation (LDA), Long Short-Term Memory (LSTM), and Snow Natural Language Processing (NLP), based on social media comments text dataset. Similar to the questionnaires published results, this article verified that 61% of consumers expressed negative sentiment toward BDPD in general. Differently, on a finer scale, this study further measured the negative sentiments that differ significantly among different topics. The measurement results show that the topics “Regular Customers Priced High” (69%) and “Usage Intention” (67%) occupy the top two places of negative sentiment among consumers, and the topic “Precision Marketing” (42%) is at the bottom. Moreover, semantic recognition results that 49% of consumers’ comments involve multiple topics, indicating that consumers have a pretty clear cognition of the complex status of the BDPD. Importantly, this study found some topics that had not been focused on in previous studies, such as more than 8% of consumers calling for government and legal departments to regulate BDPD behavior, which indicates that quite enough consumers are losing confidence in the self-discipline of the platform enterprises. Another interesting result is that consumers who pursue solutions to the BDPD belong to two mutually exclusive groups: government protection and self-protection. The significance of this study is that it reminds the e-commerce platforms to pay attention to the potential harm for consumers’ psychology while bringing additional profits through the BDPD. Otherwise, the negative consumer attitudes may cause damage to brand image, business reputation, and the sustainable development of the platforms themselves. It also provides the government supervision departments an advanced analysis method reference for more effective administration to protect social fairness.

In recent years, as people’s awareness of energy conservation, environmental protection, and sustainable development has increased, discussions related to electric vehicles (EVs) have aroused public debate on social media. At some point, most consumers face the possible risks of EVs—a critical psychological perception that invariably affects sales of EVs in the consumption market. This paper chooses to deconstruct customers’ perceived risk from third-party comment data in social media, which has better coverage and objectivity than questionnaire surveys. In order to analyze a large amount of unstructured text comment data, the natural language processing (NLP) method based on machine learning was applied in this paper. The measurement results show 15 abstracts in five consumer perceived risks to EVs. Among them, the largest number of comments is that of “Technology Maturity” (A13) which reached 25,329, and which belongs to the “Performance Risk” (PR1) dimension, indicating that customers are most concerned about the performance risk of EVs. Then, in the “Social Risk” (PR5) dimension, the abstract “Social Needs” (A51) received only 3224 comments and “Preference and Trust Rank” (A52) reached 22,324 comments; this noticeable gap indicated the changes in how consumers perceived EVs social risks. Moreover, each dimension’s emotion analysis results showed that negative emotions are more than 40%, exceeding neutral or positive emotions. Importantly, customers have the strongest negative emotions about the “Time Risk” (PR4), accounting for 54%. On a finer scale, the top three negative emotions are “Charging Time” (A42), “EV Charging Facilities” (A41), and “Maintenance of Value” (A33). Another interesting result is that “Social Needs” (A51)’s positive emotional comments were larger than negative emotional comments. The paper provides substantial evidence for perceived risk theory research by new data and methods. It can provide a novel tool for multi-dimensional and fine-granular capture customers’ perceived risks and negative emotions. Thus, it has the potential to help government and enterprises to adjust promotional strategies in a timely manner to reduce higher perceived risks and emotions, accelerating the sustainable development of EVs’ consumption market in China.

Online gaming constitutes an indispensable facet of China’s digital economy, catalyzing consumer discussions on social media platforms. This study employs a comprehensive natural language processing framework, encompassing topic mining, multi-label classification, and sentiment analysis, to evaluate consumers’ psychological perceptions of the risks associated with online games through social media comments. This study identifies 11 distinct perceived risk topics, including “Excessive Temptation”, “Entry Regulation”, and “Culture Implantation”. Numerous comments encompass multiple topics, each infused with diverse emotional inclinations, thus unveiling disparate consumer perspectives. These findings underscore the critical significance of addressing potential perceived risks and mitigating negative consumer emotions for enterprises operating within online gaming. Such measures are pivotal to maintaining a brand image, business reputation, and enduring growth. Furthermore, this study extends valuable insights to regulatory bodies, contributing to enhancing administrative efficiency, safeguarding consumer rights, and fostering a robust and sustainable trajectory within China’s online gaming market.

Online game products have fueled the boom in China’s digital economy. Meanwhile, its public health concerns have sparked discussion among consumers on social media. However, past research has seldom studied the public health topics caused by online games from the perspective of consumer opinions. This paper attempts to identify consumers’ opinions on the health impact of online game products through non-structured text and large-size social media comments. Thus, we designed a natural language processing (NLP) framework based on machine learning, which consists of topic mining, multi-label classification, and sentimental analysis. The hierarchical clustering method-based topic mining procedure determines the compatibility of this study and previous research. Every three topics are identified in “Personal Health Effects” and “Social Health Effects”, respectively. Then, the multi-label classification model’s results show that 61.62% of 327,505 comments have opinions about the health effects of online games. Topics “Adolescent Education” and “Commercial Morality” occupy the top two places of consumer attention. More than 31% of comments support two or more topics, and the “Adolescent Education” and “Commercial Morality” combination also have the highest co-occurrence. Finally, consumers expressed different emotional preferences for different topics, with an average of 63% of comments expressing negative emotions related to the health attributes of online games. In general, Chinese consumers are most concerned with adolescent education issues and hold the strongest negative emotion towards the commercial morality problems of enterprises. The significance of research results is that it reminds online game-related enterprises to pay attention to the potential harm to public health while bringing about additional profits through online game products. Furthermore, negative consumer emotions may cause damage to brand image, business reputation, and the sustainable development of the enterprises themselves. It also provides the government supervision departments with an advanced analysis method reference for more effective administration to protect public health and promote the development of the digital economy.

This paper aims to propose a hybrid collaborative development model based on multi-dimensional behavioral information (HCDMB) to deal with systemic problems in modern software engineering, such as the low efficiency of cross-stage collaboration, the fragmentation of the intelligent tool chain, and the imperfect human–machine collaboration mechanism. This paper focuses on the stages of requirements analysis, software development, software testing and software operation and maintenance in the process of software development. By integrating the multi-dimensional characteristics of the development behavior track, collaboration interaction record and product application data in the process of project promotion, the mixture of experts (MoE) model is introduced to break through the rigid constraints of the traditional tool chain. Reinforcement learning combined with human feedback is used to optimize the MoE dynamic routing mechanism. At the same time, the few-shot context learning method is used to build different expert models, which further improve the reasoning efficiency and knowledge transfer ability of the system in different scenarios. The HCDMB model proposed in this paper can be viewed as an important breakthrough in the software engineering collaboration paradigm, so as to provide innovative solutions to the many problems faced by dynamic requirements and diverse scenarios based on artificial intelligence technology in the field of software engineering involving different project personnel.

Portable power banks, as a cornerstone of China’s mobile and shared energy market, are closely integrated into daily life and work. The recent, ongoing exposure of safety incidents has led to stricter control measures in specific industries and regions, and even to the revision of relevant administrative management standards, posing a significant challenge to an industry with over a decade of rapid growth. The expectations of the public and government administrative management for product safety, reliability, and service quality have risen rapidly. Therefore, real-time public opinion monitoring is vital for supporting technological innovation, safety governance, and the sustainable development of the industry. However, traditional opinion mining methods struggle to interpret short, noisy, and user-generated data and often lack generalizable semantic understanding. This study therefore develops an LLM-driven framework that first clusters LLM-augmented multi-view representations via graph-based consensus spectral clustering to discover latent topics, followed by multi-label topic assignment and aspect-based sentiment analysis through multi-LLM consensus voting. A total of 47,023 public comments were collected from China’s leading social media platforms, yielding 12 key discussion topics covering electrical safety, charging performance, device durability, shared rental experiences, and emerging magnetic attachment design. The results show that there are strong negative views on electrical safety (86.1%) and the convenience of shared rental services (78.4%), while the views on magnetic connection innovation are mainly positive (76.6%). The topic-event correlation analysis further indicates that opinion fluctuations are closely related to pricing disputes and security-related events. This study demonstrates the effectiveness of LLM-enhanced semantic modeling in public opinion monitoring in the consumer electronics energy field. The results offer reference value for emerging industry market norms under discussion, while the proposed framework can further support the sustainability of portable energy market development by guiding product optimization, risk early warning, and data-informed regulatory strategies.