小学生羞怯特质预测及语言风格模型构建

doi:10.3724/SP.J.1041.2021.00155

摘要/Abstract

摘要：

收集在线教学平台上1306名小学生的作文、日记及评论, 采用自然语言处理技术进行文本分析, 并应用机器学习模型实现对羞怯特质的自动预测, 构建小学生羞怯行为、认知和情绪的语言风格模型。研究发现：(1)扩充的心理词典适合分析小学生文本; (2)分别存在羞怯行为、认知和情绪问题的学生其日常用语既有共性也有特性, 且与普通学生存在差异; (3)羞怯各维度在不同分类器上达到较好的预测效果, 其中随机森林模型的整体表现相对最好。

关键词: 羞怯, 在线写作, 心理词典, 文本挖掘, 语言风格模型

Abstract:

The present study aimed to explore a new method of measuring shyness based on 1306 elementary school students’ online writing texts. A supervised learning method was used to map students' labels (tagged by their results of scale) with their text features (extracted from online writing texts based on a psychological dictionary) to build a machine learning model. Key feature sets for different dimensions of shyness were built and a machine learning model was constructed based on the selected feature to achieve automatic prediction.

The labels were obtained through “National School Children Shyness Scale” completed online by elementary students. The scale includes three dimensions of shyness: shy behavior, shy cognition and shy emotion. Students with Z-scores of each dimension over 1 were labeled as shy and others were labeled as normal. Students’ online writing texts were collected from "TeachGrid" (https://www.jiaokee.com/), an online learning platform wherein students writing texts.

The dictionary applied in the present study was Textmind, a widely used Chinese psychological dictionary developed based on Linguistic Inquiry and Word Count (LIWC). The dictionary was compiled mainly based on the corpus of adults. To ensure the validity of extracted features, we modified the original dictionary by expanding the categories and vocabulary with the real writing text of elementary students. The revised dictionary contained 118 categories.

Features were extracted based on the revised dictionary. Chi-square algorithm was applied to identify the features that can distinguish between shy and normal groups to the greatest extent. Three sets of key features confirmed a significant lexical difference between shy and normal individuals. Among the selected features, some were shared by multiple dimensions reflecting the universal textual expression of shy individuals (e.g., The average number of words per sentence and the frequency of social words of shy individuals were less than that of normal counterparts.), and there were certain features reflected the unique characteristics of certain dimension (Perception words predicted shy behavior reflecting that high shy behavior individuals frequently felt being watched).

Based on the selected features, Python 3.6.2 was used to construct the six prediction modes: Decision Tree, Random Forest, Support Vector Machine, Logistic Stitch Regression, K-Nearest Neighbor and Multilayer Perceptron. Overall, random forests have achieved the best results in the present study. The F1 score was 0.582, 0.552 and 0.545 for behavior cognition and emotion showing the feasibility of automatically predicting shyness characteristics of elementary school students based on textual language. The implication of word embedding, and deep learning models would improve the final prediction.

Key words: shyness, online writing, psychological dictionary, text mining, language style model

中图分类号:

B844

骆方, 姜力铭, 田雪涛, 肖梦格, 马彦珍, 张生. (2021). 小学生羞怯特质预测及语言风格模型构建. 心理学报, 53(2), 155-169.

LUO Fang, JIANG Liming, TIAN Xuetao, XIAO Mengge, MA Yanzhen, ZHANG Sheng. (2021). Shyness prediction and language style model construction of elementary school students. Acta Psychologica Sinica, 53(2), 155-169.

图/表 9

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样本数	羞怯行为		羞怯认知		羞怯情绪
样本数	是	否	是	否	是	否
训练样本数	138	907	228	817	176	869
测试样本数	38	223	53	208	41	220

样本数	羞怯行为		羞怯认知		羞怯情绪
样本数	是	否	是	否	是	否
训练样本数	138	907	228	817	176	869
测试样本数	38	223	53	208	41	220

	实际属于羞怯群体	实际属于普通群体
分类后属于羞怯群体	TP	FP
分类属于普通群体	FN	TN

	实际属于羞怯群体	实际属于普通群体
分类后属于羞怯群体	TP	FP
分类属于普通群体	FN	TN

变量	教师评分- 羞怯行为	教师评分- 羞怯认知	教师评分- 羞怯情绪	教师评分-总分
学生自评- 羞怯行为	0.205	0.514	0.419	0.415
学生自评- 羞怯认知	-0.170	0.213	-0.092	-0.008
学生自评- 羞怯情绪	0.067	0.332	0.084	0.181
学生自评- 总分	0.058	0.421	0.161	0.239