基于文本分析的在线课程画像研究

Research on Online Course Portrait Based on Text Analysis

[目的/意义]在“互联网+教育”的时代,网络课程丰富,类型众多,学习者难以快速找到适合的在线课程。传统在线课程简介无法提供适配性引导,而课程画像能描述课程整体定义,满足学习者差异化课程适配需求。[方法/过程]基于文本分析建立相关语言模型,构建在线课程画像。以学习者在线评论文本作为数据集,从课程基本信息、联合主题模型、情感判别三个维度构建课程画像的概念模型。联合主题模型先通过基于词向量的Word2Vec算法计算词语之间的相关性,构建初始相似词库;接下来结合K-means文本聚类算法从两个维度提取评论主题;最后利用ROST_CM6软件进行评论文本情感判别并解析语义网络,数据可视化后得到课程画像。[结果/结论]最终画像能清晰呈现学习者视角的课程描述,促进整体学习效率。

[Purpose/significance]In the era of“Internet+Education”,online courses are rich and diverse,and it is difficult for learners to quickly find suitable online courses.While the traditional online course introduction cannot provide adaptive guidance,the course portrait can describe the overall definition of the course and meet the needs of learners’differentiated course adaptation.[Method/process]Based on text analysis,this paper establishes relevant language models and constructs the online course portrait.This paper takes the learners’online review text as the data set,and constructs the conceptual model of the course portrait from three dimensions:the basic information of the course,the joint topic model and the emotion discrimination.The joint topic model first calculates the correlation between words through the Word2Vec algorithm based on word vector,and constructs the initial similar lexicon;next,combined with the K-means text clustering algorithm,the comment topic is extracted from two dimensions;finally,ROST_CM6 software is used to judge the sentiment of the comment text and analyze the semantic network.After data visualization,the course portrait is obtained.[Result/conclusion]The final portrait can clearly present the course description from the learner’s perspective and promote the overall learning efficiency.