Content analysis of e-petitions with topic modeling: How to train and evaluate LDA models?

E-petitions have become a popular vehicle for political activism, but studying them has been difficult because efficient methods for analyzing their content are currently lacking. Researchers have used topic modeling for content analysis, but current practices carry some serious limitations. While modeling may be more efficient than manually reading each petition, it generally relies on unsupervised machine learning and so requires a dependable training and validation process. And so this paper describes a framework to train and validate Latent Dirichlet Allocation (LDA), the simplest and most popular topic modeling algorithm, using e-petition data. With rigorous training and evaluation, 87% of LDA-generated topics made sense to human judges. Topics also aligned well with results from an independent content analysis by the Pew Research Center, and were strongly associated with corresponding social events. Computer-assisted content analysts can benefit from our guidelines to supervise every process of training and evaluation of LDA. Software developers can benefit from learning the demands of social scientists when using LDA for content analysis. These findings have significant implications for developing LDA tools and assuring validity and interpretability of LDA content analysis. In addition, LDA topics can have some advantages over subjects extracted by manual content analysis by reflecting multiple themes expressed in texts, by extracting new themes that are not highlighted by human coders, and by being less prone to human bias.     

基于句子关系图的网页文本主题句抽取*

针对网页文本结构信息少、噪声大的特点,将句子看作点,将句子间的相似性看作边,用句子关系图描述文本中句子间的关系。抽取文本主题句的任务转化为搜索图中边最多的点。利用语义词典,将句子相似度定义为句子语义相似度,解决短文本词频相似度低的问题。选用互联网公开语料进行测试,抽取的主题句达到平均80.6%的可接受性。     

我国博客研究现状分析

论文通过整理分析我国博客研究的文献,分析我国博客研究的发展、主题内容和学科分布的情况,总结我国博客研究的特点,为博客和博客研究的良性发展提供参考.     

国外主题图和RDF的数据互操作方法研究述评

论文对主题图(Topic Maps)和资源描述框架RDF在语义网模型中的对应关系进行了分析。通过跟踪有关国外对于二者互操作的研究,分别分析了对象映射、语义映射、本体映射的映射方案,最后指出关于两者数据互操作还需要进一步解决的问题。     

行业期刊提升选题策划质量的途径——以《中国环境管理》的实践为例

【目的】 探讨行业期刊提升选题策划质量的途径。【方法】 结合《中国环境管理》的实践探索,对行业期刊快速积累选题资源的途径进行了梳理。【结果】 行业期刊应尽可能发挥选题的集聚效应、注重时效性和可读性。《中国环境管理》的稿件质量有较大改善,同时行业影响力逐步提升。【结论】 提升选题策划质量是塑造行业期刊品牌的有效途径。     

Improving topic disentanglement via contrastive learning

With the emergence and development of deep generative models, such as the variational auto-encoders (VAEs), the research on topic modeling successfully extends to a new area: neural topic modeling, which aims to learn disentangled topics to understand the data better. However, the original VAE framework had been shown to be limited in disentanglement performance, bringing their inherent defects to a neural topic model (NTM). In this paper, we put forward that the optimization objectives of contrastive learning are consistent with two important goals (alignment and uniformity) of well-disentangled topic learning. Also, the optimization objectives of contrastive learning are consistent with two key evaluation measures for topic models, topic coherence and topic diversity. So, we come to the important conclusion that alignment and uniformity of disentangled topic learning can be quantified with topic coherence and topic diversity. Accordingly, we are inspired to propose the Contrastive Disentangled Neural Topic Model (CNTM). By representing both words and topics as low-dimensional vectors in the same embedding space, we apply contrastive learning to neural topic modeling to produce factorized and disentangled topics in an interpretable manner. We compare our proposed CNTM with strong baseline models on widely-used metrics. Our model achieves the best topic coherence scores under the most general evaluation setting (100% proportion topic selected) with 25.0%, 10.9%, 24.6%, and 51.3% improvements above the second-best models’ scores reported on four datasets of 20 Newsgroups, Web Snippets, Tag My News, and Reuters, respectively. Our method also gets the second-best topic diversity scores on the dataset of 20Newsgroups and Web Snippets. Our experimental results show that CNTM can effectively leverage the disentanglement ability from contrastive learning to solve the inherent defect of neural topic modeling and obtain better topic quality.     

Topic Driven Adaptive Network for cross-domain sentiment classification

As a hot spot these years, cross-domain sentiment classification aims to learn a reliable classifier using labeled data from a source domain and evaluate the classifier on a target domain. In this vein, most approaches utilized domain adaptation that maps data from different domains into a common feature space. To further improve the model performance, several methods targeted to mine domain-specific information were proposed. However, most of them only utilized a limited part of domain-specific information. In this study, we first develop a method of extracting domain-specific words based on the topic information derived from topic models. Then, we propose a Topic Driven Adaptive Network (TDAN) for cross-domain sentiment classification. The network consists of two sub-networks: a semantics attention network and a domain-specific word attention network, the structures of which are based on transformers. These sub-networks take different forms of input and their outputs are fused as the feature vector. Experiments validate the effectiveness of our TDAN on sentiment classification across domains. Case studies also indicate that topic models have the potential to add value to cross-domain sentiment classification by discovering interpretable and low-dimensional subspaces.     

信息可视化与知识组织*

讨论信息可视化、知识组织的基本问题，并结合实例分析主题地图和本体的可视化。     

New journals for publishing medical case reports

Because they do not rank highly in the hierarchy of evidence and are not frequently cited, case reports describing the clinical circumstances of single patients are seldom published by medical journals. However, many clinicians argue that case reports have significant educational value, advance medical knowledge, and complement evidence-based medicine. Over the last several years, a vast number (∼160) of new peer-reviewed journals have emerged that focus on publishing case reports. These journals are typically open access and have relatively high acceptance rates. However, approximately half of the publishers of case reports journals engage in questionable or “predatory” publishing practices. Authors of case reports may benefit from greater awareness of these new publication venues as well as an ability to discriminate between reputable and non-reputable journal publishers.     

现代思维方式在体育科研选题中的应用

以介绍现代思维方式的基本特征，特别是创造性思维的特点为基础，结合多方面的知识和经验，探讨现代思维方式在体育科技研究选题中的应用。体育科技研究选题的方式可分为重复模仿、文献综述、移值嫁接及思索创新等几种主要形式。另外，还应充分注意两个关健环节，即课题的可行性与课题的价值及系统连续性。