Harvesting Insights: Sentiment Analysis on Smart Farming YouTube Comments for User Engagement and Agricultural Innovation

IF 2.1 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Abhishek Kaushik;Sargam Yadav;Shubham Sharma;Kevin McDaid
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引用次数: 0

Abstract

Standard farming procedures have been enhanced with the integration of information and communication technologies (ICTs), such as sensors and wireless sensor networks (WSNs), to improve efficiency. This study delves into the observations derived from comments made on YouTube channels pertaining to the topic of smart farming. We further investigate the utilization of machine learning techniques to automate the analysis of comments. In addition, this work utilizes four feature vectorization techniques and nine machine learning models to perform sentiment analysis on a data set of comments. The support vector machine radial basis function (SVM-R) classifier, when combined with the term frequency (TF) vectorizer, gets the highest macro-F1 score of 0.6683. The explainable artificial intelligence (XAI) technique, called local interpretable model-agnostic explanations (LIMEs), has been utilized to gain insights into the outcomes of the highest-performing model.
收获洞察:对智能农业 YouTube 评论进行情感分析,促进用户参与和农业创新
随着传感器和无线传感器网络(WSN)等信息和通信技术(ICTs)的集成,标准耕作程序得到了加强,从而提高了效率。本研究深入研究了从 YouTube 频道上有关智能农业主题的评论中得出的观察结果。我们进一步研究了如何利用机器学习技术自动分析评论。此外,本研究还利用四种特征向量技术和九种机器学习模型对评论数据集进行情感分析。支持向量机径向基函数(SVM-R)分类器与词频(TF)向量器相结合,获得了最高的 0.6683 宏-F1 分数。可解释的人工智能(XAI)技术,即本地可解释的模型-不可知解释(LIMEs),被用来深入了解表现最好的模型的结果。
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来源期刊
IEEE Technology and Society Magazine
IEEE Technology and Society Magazine 工程技术-工程:电子与电气
CiteScore
3.00
自引率
13.60%
发文量
72
审稿时长
>12 weeks
期刊介绍: IEEE Technology and Society Magazine invites feature articles (refereed), special articles, and commentaries on topics within the scope of the IEEE Society on Social Implications of Technology, in the broad areas of social implications of electrotechnology, history of electrotechnology, and engineering ethics.
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