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  • 标题:TOPIC MODELING AND SENTIMENT ANALYSIS IN FACEBOOK TO ENHANCE STUDENTS� LEARNING
  • 本地全文:下载
  • 作者:TAOUFIQ ZARRA ; RADDOUANE CHIHEB ; RDOUAN FAIZI
  • 期刊名称:Journal of Theoretical and Applied Information Technology
  • 印刷版ISSN:1992-8645
  • 电子版ISSN:1817-3195
  • 出版年度:2016
  • 卷号:94
  • 期号:1
  • 出版社:Journal of Theoretical and Applied
  • 摘要:Information and communication technologies (ICT) have changed our daily lives and have particularly influenced the field of education, revolutionizing the means of teaching and learning. This article focuses mainly on the blended learning, whose on-site, lessons are devoted to the essential matter offered to the learners; and the ICT are used for a deep learning. We will use the collective intelligence that is shared on social websites such as Facebook in order to create learning groups. To make this objective possible, we will base our work on the generative probabilistic model of Latent Dirichlet Allocation. We will use this model on all the discussions shared between learners and between learners and teachers to classify students according to the topics and the difficulties that each one of them has expressed; in order to help the teachers build new knowledge on the degree of assimilation of students
  • 关键词:Sentiment analysis; Latent Dirichlet Allocation; Learning; Facebook
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