<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Public participation serves as a critical component of rural health interventions and epitomizes the full realization of people’s democracy. Consequently, it is essential to tailor rural health construction based on public feedback. This study introduces an opinion mining model based on Long Short-Term Memory (LSTM) networks, designed to extract public opinions from intelligent media platforms. The methodology includes data preprocessing through text filtering, word segmentation, and lexical tagging to prepare the data for analysis. To enhance the model’s performance and avoid overfitting, dropout techniques were employed during training. Opinion classification was subsequently performed using a softmax function. Initial findings from the opinion mining process indicated that 38.29% of the analyzed comments expressed a negative view of rural health conditions. Following targeted interventions to address areas receiving low sentiment scores, a notable improvement in perceptions was observed. Specifically, the sentiment score concerning the attitudes of healthcare workers in the village increased by 14.75%. Additionally, enhancements in waste management practices led to a 19.34% increase in the related sentiment score, contributing to an overall rise of 19.85% in positive public sentiment. These results underscore the efficacy of employing this LSTM-based opinion-mining approach in fostering improvements in rural health environments through informed public participation.</p>
</abstract>

Public participation serves as a critical component of rural health interventions and epitomizes the full realization of people’s democracy. Consequently, it is essential to tailor rural health construction based on public feedback. This study introduces an opinion mining model based on Long Short-Term Memory (LSTM) networks, designed to extract public opinions from intelligent media platforms. The methodology includes data preprocessing through text filtering, word segmentation, and lexical tagging to prepare the data for analysis. To enhance the model’s performance and avoid overfitting, dropout techniques were employed during training. Opinion classification was subsequently performed using a softmax function. Initial findings from the opinion mining process indicated that 38.29% of the analyzed comments expressed a negative view of rural health conditions. Following targeted interventions to address areas receiving low sentiment scores, a notable improvement in perceptions was observed. Specifically, the sentiment score concerning the attitudes of healthcare workers in the village increased by 14.75%. Additionally, enhancements in waste management practices led to a 19.34% increase in the related sentiment score, contributing to an overall rise of 19.85% in positive public sentiment. These results underscore the efficacy of employing this LSTM-based opinion-mining approach in fostering improvements in rural health environments through informed public participation.

Rural health intervention research based on public participation: the application and effect evaluation of smart media

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Public participation serves as a critical component of rural health interventions and epitomizes the full realization of people’s democracy. Consequently, it...

Rural health intervention research based on public participation: the application and effect evaluation of smart media

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 18. Apr. 2024

Akzeptiert: 08. Juli 2024

DOI: https://doi.org/10.2478/amns-2024-1902

Schlüsselwörter
Opinion mining, LSTM, Rural health, Public participation

© 2024 Zhenghan Gao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Rural health intervention research based on public participation: the application and effect evaluation of smart media

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 18. Apr. 2024

Akzeptiert: 08. Juli 2024

DOI: https://doi.org/10.2478/amns-2024-1902

SchlüsselwörterOpinion mining, LSTM, Rural health, Public participation

© 2024 Zhenghan Gao et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Opinion mining, LSTM, Rural health, Public participation