Evidence map›Paper›PMID 37860521›Full record

ArticleHeliyon2023

Big data sentiment analysis of business environment public perception based on LTP text classification --Take Heilongjiang province as an example.

Kan Liu, Xueying Sun, Hongrui Zhou

Abstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

3 authors.

Kan LiuSchool of Management, Harbin University of Commerce, Heilongjiang, 150028, China.
Xueying SunSchool of Management, Harbin University of Commerce, Heilongjiang, 150028, China.
Hongrui ZhouSchool of Management, Harbin University of Commerce, Heilongjiang, 150028, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

At present, the research of business environment is limited to conducting surveys on specific groups or measuring data from official databases. The assessment of the business environment largely depends on public perception. Aiming to explore the public perception of business environment, this paper organically combines the big data text mining and sentiment analysis (SA). The results show that the combination of big data text mining and SA can reflect the theme characteristics, reduce the bias of sentiment and text analysis, and clearly show the public perception of the business environment. The empirical study found that the public perception of business environment depends on not only the four dimensions of business environment, but also the influence of public opinion that cannot be ignored. The public's low recognition of the business environment in Heilongjiang Province mainly includes backward economic development, serious brain drain, low government efficiency, imperfect policies, administrative law enforcement, regional climate and urban construction. In order to solve these problems, it is necessary to improve the high-standard market system to promote economic development, enhance the efficiency of government services, improve government policies, effectively enhance law enforcement, strengthen infrastructure construction and promote cultural innovation.

Indexed as

Big data text miningBusiness environmentPublic perceptionSentiment analysis

Identifiers

PMID37860521
PMCPMC10582507

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.