摘要
【目的/意义】为重塑图书馆馆员职业的良好形象,挖掘公众对于图书馆职业认知,为图书馆职业招聘和运营管理提供参考。【方法/过程】基于深度学习方法,以知乎和搜狗问问中与图书馆馆员职业相关的问答与评论为数据样本,利用BERTopic主题模型提取公众对图书馆馆员的职业形象感知,并提出改进TextCNN的情感分类模型,分析公众对图书馆馆员职业形象感知的细粒度情感。【结果/结论】结果发现:公众对图书馆馆员职业形象感知包括职业发展与前景、工作内容、工资待遇、工作时间以及工作环境与位置5个主题维度;大部分公众对图书馆馆员职业形象的情感持正向看法;各主题维度下细粒度的情感形象与图书馆馆员整体形象占比基本趋同,但在不同维度上略有侧重。【创新/局限】将BERTopic主题和改进了TextCNN算法用于图书馆馆员职业形象文本主题提取和细粒度情感分析具有一定的创新性,但是未能够进行算法性能的比较与评估。未来可以进一步地开展深度学习算法性能评估和比较。
【Purpose/significance】In order to reshape the good professional image of librarians,tap into the public's understanding of the library profession,and provide reference for the recruitment and operation management of library professions.【Method/process】Based on deep learning methods,data samples are collected from Q&A and comments related to the profession of library librarians in Zhihu and Sogou Questioning.This paper uses BERTopic Topic model mining to extract the public's perception of librarians'professional image,and proposes an improved TextCNN emotional classification model to analyze the public's fine-grained emotional evaluation of librarians'professional image perception.【Result/conclusion】The results showed that the public's perception of the professional image of librarians includes five thematic dimensions:career development prospects,job content,salary,working hours,and work environment and location;Most of the public have a positive emotional view on the professional image of librarians.The proportion of fine-grained emotional images in various thematic dimensions and the overall image of librarians is basically similar,but there is a slight emphasis on different dimensions.【Innovation/limitation】The use of BERTopic theme and improved TextCNN algorithm for extracting professional image text themes and fine-grained sentiment analysis of librarians has certain innovation,However,it was not possible to compare and evaluate the algorithm performance.In the future,further performance evaluation and comparison of deep learning algorithms can be carried out.
作者
张善美
ZHANG Shanmei(Library,Qufu Normal University,Rizhao 276826,China)
出处
《情报科学》
CSSCI
北大核心
2023年第11期18-27,共10页
Information Science
基金
日照市社会科学基金重点项目“‘城市+大学’战略下的公共图书馆科技情报服务研究”。
