Evidence map›Paper›PMID 42748078›Full record

ArticlePloS one2026

Large language model enhanced public opinion monitoring for China's portable energy market: A data driven analysis of social media comments.

Yun Zhang, Weikang Xie, Xiao Li, Wei Liao, Tao Shu, Jixian Zhou, Jun Wang

Abstract read
In one paragraph

Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yun ZhangCollege of Artificial Intelligence, Chengdu University of Information Technology, Chengdu, China.ORCID https://orcid.org/0009-0001-2703-4222
Weikang XieSchool of Software Engineering, Chengdu University of Information Technology, Chengdu, China.ORCID https://orcid.org/0009-0003-2355-0128
Xiao LiSchool of Software Engineering, Chengdu University of Information Technology, Chengdu, China.ORCID https://orcid.org/0009-0009-2764-6251
Wei LiaoSchool of Software Engineering, Chengdu University of Information Technology, Chengdu, China.ORCID https://orcid.org/0000-0001-6268-0695
Tao ShuSchool of Software Engineering, Chengdu University of Information Technology, Chengdu, China.ORCID https://orcid.org/0000-0003-0417-2814
Jixian ZhouSchool of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, China.ORCID https://orcid.org/0000-0002-6783-3096
Jun WangSchool of Management Science and Engineering, Southwestern University of Finance and Economics, Chengdu, China.ORCID https://orcid.org/0000-0002-2613-2752

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Electric Power SuppliesPublic OpinionSocial MediaChinaHumansLarge Language Models

Identifiers

PMID42748078
PMCPMC13580948

What OpenQuestion holds

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Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.