{"title":"Harvesting Insights: Sentiment Analysis on Smart Farming YouTube Comments for User Engagement and Agricultural Innovation","authors":"Abhishek Kaushik;Sargam Yadav;Shubham Sharma;Kevin McDaid","doi":"10.1109/MTS.2024.3455754","DOIUrl":null,"url":null,"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.","PeriodicalId":55016,"journal":{"name":"IEEE Technology and Society Magazine","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Technology and Society Magazine","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10682965/","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
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.
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.