Contextual text mining

With the dramatic growth of text information, there is an increasing need for powerful text mining systems that can automatically discover useful knowledge from text. Text is generally associated with all kinds of contextual information. Those contexts can be explicit, such as the time and the location where a blog article is written, and the author(s) of a biomedical publication, or implicit, such as the positive or negative sentiment that an author had when she wrote a product review; there may also be complex context such as the social network of the authors. Many applications require analysis of topic patterns over different contexts. For instance, analysis of search logs in the context of the user can reveal how we can improve the quality of a search engine by optimizing the search results according to particular users; analysis of customer reviews in the context of positive and negative sentiments can help the user summarize public opinions about a product; analysis of blogs or scientific publications in the context of a social network can facilitate discovery of more meaningful topical communities. Since context information significantly affects the choices of topics and language made by authors, in general, it is very important to incorporate it into analyzing and mining text data. In general, modeling the context in text, discovering contextual patterns of language units and topics from text, a general task which we refer to as Contextual Text Mining, has widespread applications in text mining. In this thesis, we provide a novel and systematic study of contextual text mining, which is a new paradigm of text mining treating context information as the ``first-class citizen.'' We formally define the problem of contextual text mining and its basic tasks, and propose a general framework for contextual text mining based on generative modeling of text. This conceptual framework provides general guidance on text mining problems with context information and can be instantiated into many real tasks, including the general problem of contextual topic analysis. We formally present a functional framework for contextual topic analysis, with a general contextual topic model and its various versions, which can effectively solve the text mining problems in a lot of real world applications. We further introduce general components of contextual topic analysis, by adding priors to contextual topic models to incorporate prior knowledge, regularizing contextual topic models with dependency structure of context, and postprocessing contextual patterns to extract refined patterns. The refinements on the general contextual topic model naturally lead to a variety of probabilistic models which incorporate different types of context and various assumptions and constraints. These special versions of the contextual topic model are proved effective in a variety of real applications involving topics and explicit contexts, implicit contexts, and complex contexts. We then introduce a postprocessing procedure for contextual patterns, by generating meaningful labels for multinomial context models. This method provides a general way to interpret text mining results for real users. By applying contextual text mining in the ``context'' of other text information management tasks, including ad hoc text retrieval and web search, we further prove the effectiveness of contextual text mining techniques in a quantitative way with large scale datasets. The framework of contextual text mining not only unifies many explorations of text analysis with context information, but also opens up many new possibilities for future research directions in text mining.

The relationship between personal cognition, social context, and knowledge sharing in global communities of practice

The purpose of this study was to identify the structural pathways of personal cognition and social context as they influence knowledge sharing behaviors in communities of practice. Based on the existing literature, ten hypotheses and a conceptual model built on the basis of the social cognitive theory were developed regarding the interrelationships of the five constructs: self-efficacy for knowledge sharing, outcome expectations, sense of community, leadership of a community, and knowledge sharing. The data were collected through an online questionnaire from the employees who have participated in communities of practice in a Fortune 100 corporation. A total of 438 usable questionnaires were collected. Overall, three analyses were conducted in order to prove the given hypotheses: (a) hypothesized measurement model fit, (b) relational and influential associations among the constructs, and (c) structural equation model analysis (SEM). In addition, open-ended responses were analyzed. The results presented that (a) hypothesized measurement models were valid and reliable, (b) personal cognitive factors, self-efficacy and outcome expectations for knowledge sharing, were found to be significant predictors of community members’ sense of community and knowledge sharing behaviors, (c) sense of community had the most significant impact on the knowledge sharing, (d) as the perceived social context, sense of community mediated the effects of personal cognition on knowledge sharing behaviors, and (e) personal cognition and social context jointly contributed to knowledge sharing. In brief, all of the hypotheses were positively supported. A conclusive summary is provided along with contributive discussion. Implications and contributions to HRD researchers and practitioners are discussed, and recommendations are provided.