The task of classifying opinions conveyed in any form of text online is referred to as sentiment analysis.The emergence of social media usage and its spread has given room for sentiment analysis in our daily lives.Soc...The task of classifying opinions conveyed in any form of text online is referred to as sentiment analysis.The emergence of social media usage and its spread has given room for sentiment analysis in our daily lives.Social media applications and websites have become the foremost spring of data recycled for reviews for sentimentality in various fields.Various subject matter can be encountered on social media platforms,such as movie product reviews,consumer opinions,and testimonies,among others,which can be used for sentiment analysis.The rapid uncovering of these web contents contains divergence of many benefits like profit-making,which is one of the most vital of them all.According to a recent study,81%of consumers conduct online research prior to making a purchase.But the reviews available online are too huge and numerous for human brains to process and analyze.Hence,machine learning classifiers are one of the prominent tools used to classify sentiment in order to get valuable information for use in companies like hotels,game companies,and so on.Understanding the sentiments of people towards different commodities helps to improve the services for contextual promotions,referral systems,and market research.Therefore,this study proposes a sentiment-based framework detection to enable the rapid uncovering of opinionated contents of hotel reviews.A Naive Bayes classifier was used to process and analyze the dataset for the detection of the polarity of the words.The dataset from Datafiniti’s Business Database obtained from Kaggle was used for the experiments in this study.The performance evaluation of the model shows a test accuracy of 96.08%,an F1-score of 96.00%,a precision of 96.00%,and a recall of 96.00%.The results were compared with state-of-the-art classifiers and showed a promising performance andmuch better in terms of performancemetrics.展开更多
职业院校教师数字素养是一项综合能力体系,涵盖了专业素养、实践素养及社会素养三大维度,其提升是响应经济社会发展、满足时代需求的关键,也是职业教育信息化2.0推进的核心。针对当前职业院校教师数字素养提升面临观念滞后、课程体系不...职业院校教师数字素养是一项综合能力体系,涵盖了专业素养、实践素养及社会素养三大维度,其提升是响应经济社会发展、满足时代需求的关键,也是职业教育信息化2.0推进的核心。针对当前职业院校教师数字素养提升面临观念滞后、课程体系不完善、制度不健全、资源投入不足等问题,文章提出强化职业院校教师主体意识、制定科学合理的保障制度、加强教师数字能力专题培训、构建数字素养提升共同体等措施,全面提升职业院校教师数字素养,促进职业教育数字化转型,培养更多适应未来需求的技术技能型人才。The digital literacy of vocational college teachers constitutes a comprehensive capability system, encompassing professional literacy, practical literacy, and social literacy. Enhancing this literacy is crucial for responding to economic and social development, meeting the demands of the era, and serving as the core of advancing Vocational Education Informatization 2.0. Currently, the enhancement of vocational college teachers’ digital literacy confronts challenges such as lagging perceptions, incomplete curriculum systems, inadequate institutional frameworks, and insufficient resource investments. This article proposes measures including strengthening teachers’ subjective awareness, establishing scientific and reasonable safeguard systems, intensifying specialized training in digital competencies, and constructing a digital literacy enhancement community. These initiatives aim to comprehensively elevate the digital literacy of vocational college teachers, facilitate the digital transformation of vocational education, and cultivate a greater number of technically skilled talents adept at meeting future demands.展开更多
文摘The task of classifying opinions conveyed in any form of text online is referred to as sentiment analysis.The emergence of social media usage and its spread has given room for sentiment analysis in our daily lives.Social media applications and websites have become the foremost spring of data recycled for reviews for sentimentality in various fields.Various subject matter can be encountered on social media platforms,such as movie product reviews,consumer opinions,and testimonies,among others,which can be used for sentiment analysis.The rapid uncovering of these web contents contains divergence of many benefits like profit-making,which is one of the most vital of them all.According to a recent study,81%of consumers conduct online research prior to making a purchase.But the reviews available online are too huge and numerous for human brains to process and analyze.Hence,machine learning classifiers are one of the prominent tools used to classify sentiment in order to get valuable information for use in companies like hotels,game companies,and so on.Understanding the sentiments of people towards different commodities helps to improve the services for contextual promotions,referral systems,and market research.Therefore,this study proposes a sentiment-based framework detection to enable the rapid uncovering of opinionated contents of hotel reviews.A Naive Bayes classifier was used to process and analyze the dataset for the detection of the polarity of the words.The dataset from Datafiniti’s Business Database obtained from Kaggle was used for the experiments in this study.The performance evaluation of the model shows a test accuracy of 96.08%,an F1-score of 96.00%,a precision of 96.00%,and a recall of 96.00%.The results were compared with state-of-the-art classifiers and showed a promising performance andmuch better in terms of performancemetrics.
文摘职业院校教师数字素养是一项综合能力体系,涵盖了专业素养、实践素养及社会素养三大维度,其提升是响应经济社会发展、满足时代需求的关键,也是职业教育信息化2.0推进的核心。针对当前职业院校教师数字素养提升面临观念滞后、课程体系不完善、制度不健全、资源投入不足等问题,文章提出强化职业院校教师主体意识、制定科学合理的保障制度、加强教师数字能力专题培训、构建数字素养提升共同体等措施,全面提升职业院校教师数字素养,促进职业教育数字化转型,培养更多适应未来需求的技术技能型人才。The digital literacy of vocational college teachers constitutes a comprehensive capability system, encompassing professional literacy, practical literacy, and social literacy. Enhancing this literacy is crucial for responding to economic and social development, meeting the demands of the era, and serving as the core of advancing Vocational Education Informatization 2.0. Currently, the enhancement of vocational college teachers’ digital literacy confronts challenges such as lagging perceptions, incomplete curriculum systems, inadequate institutional frameworks, and insufficient resource investments. This article proposes measures including strengthening teachers’ subjective awareness, establishing scientific and reasonable safeguard systems, intensifying specialized training in digital competencies, and constructing a digital literacy enhancement community. These initiatives aim to comprehensively elevate the digital literacy of vocational college teachers, facilitate the digital transformation of vocational education, and cultivate a greater number of technically skilled talents adept at meeting future demands.
