Analysis of Academic Libraries' Facebook Posts: Text and Data Analytics

This research analyzed a dataset of academic libraries' posts on Facebook. It applied a text and data analytics approach to a dataset collected from the Facebook posts of academic libraries at the top 100 English-speaking universities, as listed by the 2014 Shanghai World University Rankings. The dataset is from a two-year posting history of 18,333 unique posts, 113,621 likes, and 3401 comments. Less than a quarter of the libraries had more than 2000 post-related likes, and only seven received more than 100 comments on their postings. Content analysis identified the most prevalent single word (unigrams), bigrams (two-word sequences), and trigrams (three-word sequences) in high and low engagement content. Semantic analysis identified the semantic categories for posts with high and low engagement. The findings can assist academic libraries in their social media strategies for engagement, marketing, and visibility.     

对外国经典译著的编辑思考

把高质量的译本奉献给读者,是从事外国经典译著编辑的终极目标。在编辑外国经典译著时一定要忠于原文,与译者交朋友,勤查各类工具书,重视读者意见,注意自身素质的提高,关注各类翻译赛事以发现和培养优秀译者。     

标记系统及学术文本处理的未来（一）

标记事务影响着学者们对思考与写作系统的采纳使用。程序性标记和表示性标记会阻碍这一进程的发展,描述性标记则通过简化机械性任务,让学者们将注意力集中于内容上的做法,以加快这一进程。     

科技期刊编辑新媒体出版能力的培育

分析我国科技期刊编辑现状和新时期编辑职业能力的新要求,通过对出版格局的统筹策划能力、专业领域的预研判断能力、新媒体技术的开发应用能力、出版大数据的挖掘分析能力、出版实践的组织协调能力、面向国际战略的综合拓展能力等6个方面的解析,提出了当今科技期刊编辑所应具备和培养的全媒体出版的适应能力.     

面向数字人文研究的大规模古籍文本可视化分析与挖掘

传统的古籍开发与应用模式已难以适应人文学科研究的需要,人文学科研究者期待一个技术逻辑和人文逻辑相耦合的数字人文研究范式的出现。本文从古籍文献深层次开发与利用出发,利用新的信息技术与面向数字人文研究跨学科方法,以大规模中国古籍文本为研究对象,采用大数据研究理念,对古籍进行整理、标注、自动分词等处理,以词频分析统计为研究核心,采用数据降噪、基于窗口时间单位的统计分析计算、滑动窗口预测等分析与挖掘方法,采用大数据实时分析技术,实现了实时、在线、立体、可视化、定量分析字词的历史词频分布规律,创建了一个以语言学、历史文献学、历史地理学等人文学科研究为主的古籍实时统计分析平台,可辅助研究者在大量的古籍文献中发现新的模式、现象、趋势等,实现古籍开发与应用模式创新的初步尝试。图11。参考文献36。     

网络数据挖掘及其在面向Web的知识检索中的应用

介绍了网络数据挖掘的含义和相关技术，并着重介绍了网络数据挖掘方法在面向Web的知识检索中的应用：通过对信息源内容与结构的挖掘，对用户访问记录的挖掘，对专家访问的挖掘，发现隐藏在网络数据流背后的知识关联和知识，形成特定知识库，支持知识检索。     

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��[Purpose/significance] There are many knowledge units in the network community, among which there are intricate relationships. It is necessary to carry out multiple knowledge relations mining uniformly and succinctly on the premise of retaining all the relations of knowledge units.[Method/process] This paper puts forward the solution of multi-knowledge relations mining in network community. Firstly, 3 typical knowledge units (users, texts and words) in the network community and their multiple relations in the knowledge communication were extracted into a supernetwork. Secondly, the network representation learning algorithm was used to uniformly represent the nodes in the supernetwork as low-dimensional dense vectors. Finally, multiple knowledge relations calculation was carried out based on nodal vector.[Result/conclusion] The effectiveness of the scheme was verified by taking cardiovascular BBS in dingxiang garden as an example. This scheme not only retains all the information of the knowledge unit, but also carries out the mining of the knowledge relation under the unified low-dimensional characteristics, and finally the knowledge relation meets the requirements of the diversity of the knowledge organization scene in the network community.     

基于DW的DSS系统的设计与实施

随着我国加入WTO ,企业所面临的外部环境正在发生迅速变化 ,这对企业的决策提出了更高的要求。文章通过分析企业现有决策方式和OLTP系统存在的问题 ,介绍了决策支持系统技术的研究现状和发展趋势 ,阐述了基于数据仓库的决策支持系统的关键技术和设计与实现方法 ,提出了企业在DSS建设中需注意的一些问题     

文本挖掘、数据挖掘和知识管理 ——二十一世纪的智能信息处理

本文首先介绍了数据挖掘、文本挖掘和知识管理等概念，然后从技术角度出发，将知识管理划分为知识库、知识共享和知识发现三个阶段，分析了作为最高阶段的知识发现的关键技术和意义，最后指出在文本中进行知识发现是新世纪智能信息处理的重要方向。     

Text Mining and Subject Analysis for Fiction; or,Using Machine Learning and Information Extraction to Assign Subject Headings to Dime Novels

Abstract

This article describes multiple experiments in text mining at Northern Illinois University that were undertaken to improve the efficiency and accuracy of cataloging. It focuses narrowly on subject analysis of dime novels, a format of inexpensive fiction that was popular in the United States between 1860 and 1915. NIU holds more than 55,000 dime novels in its collections, which it is in the process of comprehensively digitizing. Classification, keyword extraction, named-entity recognition, clustering, and topic modeling are discussed as means of assigning subject headings to improve their discoverability by researchers and to increase the productivity of digitization workflows.