NON-PARAMETRIC TOPIC MODEL FOR DISCOVERING GEOGRAPHICAL TOPIC VARIATIONS

This paper presents a non-parametric topic model that captures not only the latent topics in text collections, but also how the topics change over space. Unlike other recent work that relies on either Gaussian assumptions or diseretization of locations, here topics are associated with a distance dependent Chinese Restaurant Process （ddCRP）, and for each document, the observed words are influenced by the document＇s GPS-tag. Our model allows both unbound number and flexible distribution of the geographical variations of the topics＇ content. We develop a Gibbs sampler for the proposal, and compare it with existing models on a real data set basis.     

Topic tracking with Bayesian belief network

The task of topic tracking is to monitor a stream of stories and find all subsequent stories that discuss the same topic. Using Bayesian belief network we give three topic tracking models: a static topic model BSTM and two dynamic topic models BDTM-I, BDTM-II. BDTM-II merges the advantages of BSTM and BDTM-I, has better tracking performance than the former two, and effectively alleviates topic drift phenomenon. Applying unrelated coming stories to update BDTM-I and BDTM-II can filter noises existed in topics. Experiments on TDT corpora show that BSTM decreases (C_det)_norm by 5.5% comparing to VSM, BDTM-II decreases (C_det)_norm by 6.3% and 6.0% comparing to BSTM and BDTM-I respectively, using unrelated stories can improve the tracking performance.     

BAHA: A Novel Approach to Automatic Bug Report Assignment with Topic Modeling and Heterogeneous Network Analysis

We propose an approach called Bug report assignment with topic modeling and heterogeneous network analysis (BAHA) to automatically assign bug reports to developers.Existing studies adopt social network analysis to characterize the collaboration of developers.The networks used in these studies are all homogenous.In real practice of bug resolution,different developers collaborate on different bug reports that makes the homogenous network unable to capture this information.We use heterogeneous network to describe the relations between reporters,bug reports and developers to characterize developers' collaboration.Experiments on Eclipse JDT project show that BAHA outperforms the state of art methods on automatic bug report assignment.     

Twitter data analysis for studying communities of practice in the media industry

Today, more and more physical communities of practice, a concept that describes a group of people that share a passion and interact regularly at events to exchange knowledge, utilize social media, such as Twitter. Brotaru, for instance, is such a physical community of practice for media professionals in Brussels. It is a monthly meet-up of videogame developers in various locations in Brussels. Furthermore, Twitter becomes widely acknowledged as important instrument for learning and community formation in the virtual world. But, do these communities of practice use Twitter only to promote their physical activities of learning? Or, are the activities of the physical communities further extended into the virtual world meaning that virtual communities of practice emerge from them? This article suggests a novel mixed-methods approach based on qualitative and quantitative data to measure the role of Twitter for physical communities of practice. The method applies different statistical measures and analysis on harvested Twitter data and additionally brings two of the most used methods in Twitter analysis together, social network analysis and text data analysis (a.k.a., content analysis). Four different communities of practice in Brussels’ media industry and their activities and followers on Twitter have been analysed. The findings showed that the activities of the communities of practice extend into the Twitter sphere as the online communities are characterised by a shared domain, a lively community and shared practices. The analysis further revealed that Twitter offers three main opportunities for the activities of communities of practice: it offers geographical extension; it gives temporal autonomy; and, it can be used to diversify the practices.     

Semi-automatic knowledge extraction, representation and context-sensitive intelligent retrieval of video content using collateral context modelling with scalable ontological networks

Automatic indexing and retrieval of digital data poses major challenges. The main problem arises from the ever increasing mass of digital media and the lack of efficient methods for indexing and retrieval of such data based on the semantic content rather than keywords. To enable intelligent web interactions, or even web filtering, we need to be capable of interpreting the information base in an intelligent manner. For a number of years research has been ongoing in the field of ontological engineering with the aim of using ontologies to add such (meta) knowledge to information. In this paper, we describe the architecture of a system (Dynamic REtrieval Analysis and semantic metadata Management (DREAM)) designed to automatically and intelligently index huge repositories of special effects video clips, based on their semantic content, using a network of scalable ontologies to enable intelligent retrieval. The DREAM Demonstrator has been evaluated as deployed in the film post-production phase to support the process of storage, indexing and retrieval of large data sets of special effects video clips as an exemplar application domain. This paper provides its performance and usability results and highlights the scope for future enhancements of the DREAM architecture which has proven successful in its first and possibly most challenging proving ground, namely film production, where it is already in routine use within our test bed Partners’ creative processes.     

微博热点话题检测系统的设计与实现

本文描述了一个微博热点检测系统。管理者通过它可以快速了解正在发生的或是已发生的微博热点事件。系统采用调用微博API接口与改进爬虫程序相结合的方式获取网页数据;由于网络数据量巨大,为了提高效率,还采用了网页清理技术;重点介绍了话题活性模型的方法,系统可以根据时间坐标快速寻找热点话题,提高了热点话题发现的效率,大大降低了热点话题发现的时间复杂度。     

基于混合权重合并策略的社交网络用户关注点识别方法

主题模型是用于识别博客、网络社区、微博等社交网络平台上用户关注点的重要手段。考虑到社交网络平台上短文本主题识别的特殊性,该文根据短文本内容在上下文上的相关性,提出一种基于混合权重合并策略的AW-LDA模型。该模型将符合上下文相关条件的短文本进行虚拟合并,并根据上下文相关程度对不同短文本赋予不同的权重,构建了一种新的短文本主题识别方法。通过网络BBS社区与微博社区两组数据的实验,该模型能够有效识别不同话题下社交网络用户关注点,为解决短文本主题识别问题提供了新的解决思路。     

Blockchain technology as an enabler of consumer trust: A text mining literature analysis

Blockchain (BC) technology is gaining momentum in a variety of application domains, beyond finance. We present a text mining literature analysis of a body of published articles queried in the Scopus database, regarding BC technology and consumer trust. We applied a semiautomated text mining and topic modelling approach: we mix top-down and bottom-up procedures to align the existing literature on BC taxonomies with the gathered articles' list of keywords; we then feed automatically the latent Dirichlet allocation (LDA) algorithm to uncover relevant topics enabling to analyse the existing body of knowledge. Our analysis highlights the multidisciplinary nature of BC research within consumer trust. Among others, findings show pertinent aspects to consumer trust, such as traceability and privacy, are receiving only marginal attention from scholars. Our analysis also reveals the marketing, social and economic sciences’ researchers should devote efforts to the application of BC and its impact to consumer trust. We provide future research trends we deem crucial to be addressed regarding sustainable blockchain trustability.     

主题网络蜘蛛搜索策略贪婪性解决方法

主题网络蜘蛛搜索策略是专业搜索引擎的核心技术。但是目前的主题搜索算法往往存在很大贪婪性,难以在全局范围内找到最优解。通过比较分析发现Best-First算法虽然有它的不足,但是它在几种算法中表现的性能最优。故以Best-First算法为基础,提出了BS-BS算法。对BS-BS算法进行性能评价,发现应用此算法搜索不但“召回率”有所提高,还能在一定程度上找到全局范围内的最优解。