The annual turnout of faculty graduates in Nigeria is not only increasing numerically but it is ironically, unemployed and other times unemployable. This assertion is supported by researches in the social sciences and...The annual turnout of faculty graduates in Nigeria is not only increasing numerically but it is ironically, unemployed and other times unemployable. This assertion is supported by researches in the social sciences and reasons adduced to the trend range from lack of qualitative and unavailability of teaching and research facilities, as well as insufficient capable human resources among others. This paper x-rayed the case of Industrial Design graduates and their post training outcome, to understand whether or not graduates of Industrial Design are gainfully employed, unemployed, or unemployable; and why？ Research design used was survey, while the instrument administered on the sample size was structured questionnaire. Two hundred and thirty-three （233） students of Industrial Design constituted the sample size. The research questions were analyzed with the aid of Table of frequency distribution, while a non-parametric test by way of Friedman＇s two-way ANOVA was used to analyze the hypotheses. The outcome of the study revealed that, though the programme of Industrial Design is evolving as craft-based, it is viable. Graduates of the programme are not unemployable in the Nigerian labour market. Finally, the paper proffers solutions and recommendations in form of ideas and deductions to enhance the aim of the study.展开更多
The COVID-19 pandemic has spread globally,resulting in financialinstability in many countries and reductions in the per capita grossdomestic product.Sentiment analysis is a cost-effective method for acquiringsentiment...The COVID-19 pandemic has spread globally,resulting in financialinstability in many countries and reductions in the per capita grossdomestic product.Sentiment analysis is a cost-effective method for acquiringsentiments based on household income loss,as expressed on social media.However,limited research has been conducted in this domain using theLexDeep approach.This study aimed to explore social trend analytics usingLexDeep,which is a hybrid sentiment analysis technique,on Twitter to capturethe risk of household income loss during the COVID-19 pandemic.First,tweet data were collected using Twint with relevant keywords before(9 March2019 to 17 March 2020)and during(18 March 2020 to 21 August 2021)thepandemic.Subsequently,the tweets were annotated using VADER(lexiconbased)and fed into deep learning classifiers,and experiments were conductedusing several embeddings,namely simple embedding,Global Vectors,andWord2Vec,to classify the sentiments expressed in the tweets.The performanceof each LexDeep model was evaluated and compared with that of a supportvector machine(SVM).Finally,the unemployment rates before and duringCOVID-19 were analysed to gain insights into the differences in unemploymentpercentages through social media input and analysis.The resultsdemonstrated that all LexDeep models with simple embedding outperformedthe SVM.This confirmed the superiority of the proposed LexDeep modelover a classical machine learning classifier in performing sentiment analysistasks for domain-specific sentiments.In terms of the risk of income loss,the unemployment issue is highly politicised on both the regional and globalscales;thus,if a country cannot combat this issue,the global economy will alsobe affected.Future research should develop a utility maximisation algorithmfor household welfare evaluation,given the percentage risk of income lossowing to COVID-19.展开更多
文摘The annual turnout of faculty graduates in Nigeria is not only increasing numerically but it is ironically, unemployed and other times unemployable. This assertion is supported by researches in the social sciences and reasons adduced to the trend range from lack of qualitative and unavailability of teaching and research facilities, as well as insufficient capable human resources among others. This paper x-rayed the case of Industrial Design graduates and their post training outcome, to understand whether or not graduates of Industrial Design are gainfully employed, unemployed, or unemployable; and why？ Research design used was survey, while the instrument administered on the sample size was structured questionnaire. Two hundred and thirty-three （233） students of Industrial Design constituted the sample size. The research questions were analyzed with the aid of Table of frequency distribution, while a non-parametric test by way of Friedman＇s two-way ANOVA was used to analyze the hypotheses. The outcome of the study revealed that, though the programme of Industrial Design is evolving as craft-based, it is viable. Graduates of the programme are not unemployable in the Nigerian labour market. Finally, the paper proffers solutions and recommendations in form of ideas and deductions to enhance the aim of the study.
基金funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University,through the Research Groups Program Grant no.(RGP-1443-0045).
文摘The COVID-19 pandemic has spread globally,resulting in financialinstability in many countries and reductions in the per capita grossdomestic product.Sentiment analysis is a cost-effective method for acquiringsentiments based on household income loss,as expressed on social media.However,limited research has been conducted in this domain using theLexDeep approach.This study aimed to explore social trend analytics usingLexDeep,which is a hybrid sentiment analysis technique,on Twitter to capturethe risk of household income loss during the COVID-19 pandemic.First,tweet data were collected using Twint with relevant keywords before(9 March2019 to 17 March 2020)and during(18 March 2020 to 21 August 2021)thepandemic.Subsequently,the tweets were annotated using VADER(lexiconbased)and fed into deep learning classifiers,and experiments were conductedusing several embeddings,namely simple embedding,Global Vectors,andWord2Vec,to classify the sentiments expressed in the tweets.The performanceof each LexDeep model was evaluated and compared with that of a supportvector machine(SVM).Finally,the unemployment rates before and duringCOVID-19 were analysed to gain insights into the differences in unemploymentpercentages through social media input and analysis.The resultsdemonstrated that all LexDeep models with simple embedding outperformedthe SVM.This confirmed the superiority of the proposed LexDeep modelover a classical machine learning classifier in performing sentiment analysistasks for domain-specific sentiments.In terms of the risk of income loss,the unemployment issue is highly politicised on both the regional and globalscales;thus,if a country cannot combat this issue,the global economy will alsobe affected.Future research should develop a utility maximisation algorithmfor household welfare evaluation,given the percentage risk of income lossowing to COVID-19.
