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罗马尼亚小农户对鼹鼠侵扰持何种态度?一项基于社交媒体和博客讨论的主题建模与情感分析研究。

What Is the Attitude of Romanian Smallholders Towards a Ground Mole Infestation? A Study Using Topic Modelling and Sentiment Analysis on Social Media and Blog Discussions.

作者信息

Călin Alina Delia, Coroiu Adriana Mihaela

机构信息

Department of Computer Science, Babeş-Bolyai University, 1 M. Kogălniceanu Street, 400084 Cluj-Napoca, Romania.

出版信息

Animals (Basel). 2024 Dec 14;14(24):3611. doi: 10.3390/ani14243611.

DOI:10.3390/ani14243611
PMID:39765515
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11672416/
Abstract

In this paper, we analyse the attitudes and sentiments of Romanian smallholders towards mole infestations, as expressed in online contexts. A corpus of texts on the topic of ground moles and how to get rid of them was collected from social media and blog thread discussions. The texts were analysed using topic modelling, clustering, and sentiment analysis, revealing both negative and positive sentiments and attitudes. The methods used by farmers when dealing with ground moles involve both eco-friendly repellent solutions and toxic substances and pesticides. Even well-intentioned farmers are discouraged by crop and lawn damage, resorting to environmentally aggressive solutions. The study shows that the relationship between humans and moles could be improved by active education on effective ecological agricultural approaches.

摘要

在本文中,我们分析了罗马尼亚小农户在网络环境中表达的对鼹鼠侵扰的态度和情绪。我们从社交媒体和博客线程讨论中收集了关于地鼠及其防治方法的文本语料库。通过主题建模、聚类和情感分析对这些文本进行了分析,揭示了负面和正面的情绪及态度。农民在对付地鼠时所采用的方法既包括环保驱避解决方案,也包括有毒物质和杀虫剂。即使是出于善意的农民,也会因庄稼和草坪受损而气馁,从而采取对环境有较大影响的解决方案。研究表明,通过积极开展关于有效生态农业方法的教育,可以改善人与鼹鼠之间的关系。

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本文引用的文献

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Exploring climate change discourse on social media and blogs using a topic modeling analysis.运用主题建模分析探索社交媒体和博客上的气候变化话语。
Heliyon. 2024 Jun 5;10(11):e32464. doi: 10.1016/j.heliyon.2024.e32464. eCollection 2024 Jun 15.
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A Topic Modeling Comparison Between LDA, NMF, Top2Vec, and BERTopic to Demystify Twitter Posts.LDA、NMF、Top2Vec和BERTopic用于揭秘推特帖子的主题建模比较
Front Sociol. 2022 May 6;7:886498. doi: 10.3389/fsoc.2022.886498. eCollection 2022.
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The disaster of misinformation: a review of research in social media.
错误信息的灾难:社交媒体研究综述
Int J Data Sci Anal. 2022;13(4):271-285. doi: 10.1007/s41060-022-00311-6. Epub 2022 Feb 15.
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Detecting Presence of PTSD Using Sentiment Analysis From Text Data.利用文本数据中的情感分析检测创伤后应激障碍的存在
Front Psychiatry. 2022 Feb 1;12:811392. doi: 10.3389/fpsyt.2021.811392. eCollection 2021.
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Sentiment Analysis in Social Media Data for Depression Detection Using Artificial Intelligence: A Review.利用人工智能进行抑郁症检测的社交媒体数据情感分析:综述
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Oecologia. 1977 Sep;30(3):277-283. doi: 10.1007/BF01833635.
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Modeling Clustered Data with Very Few Clusters.对极少聚类的聚类数据进行建模。
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