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.

Consumerism in urban Russia in the 1880s and 90s: Exploring conceptions of time, space, and modernity through bicycle and watch advertisements

This paper discusses the urban consumer culture in Moscow and Petersburg during the 1880s and 1890s and uses the consumption of bicycles and watches as a lens through which to explore changing perceptions of time and space within the experience of modernity at the end of the nineteenth century. Specifically, I argue that the way in which consumers and merchants constructed a dialogue of meaning around particular objects; the way in which objects are consumed by a culture gives insight into the values, morals, and tenure of that culture. The paper preferences newspaper ads and photographs as the mouthpieces of merchants and consumers respectively as they constructed a dialogue in the language of consumerism, and explores the ways in which both parties sought to assign meaning to objects during the experience of modernity. I am particularly interested in the way consumers perform elements of cultural modernity in photographs and how these instances of performance relate to their negotiation of modernity. The paper takes as its focus large section of the urban Russian population, much of whom can traditionally be called “middle class” but whose diversity has led me to the adoption of the term “consumer community,” and whose makeup is described in detail. The paper contributes to the continuing scholarly discourse on the makeup of the middle class in Russia and the social boundaries of late tsarist society. It speaks to the the developing sensibilities and values of a generation struggling to define itself in a rapidly changing world, to the ways in which conceptualizations of public and private space, as well as feminine and masculine space were redefined, and to the developing visual culture of the Russian consumer society, largely predicated on the display of objects to signify socially desirable traits. Whereas other explorations of consumer culture and advertisements have portrayed the relationship between merchants and consumers as a one-sided monologue in which merchants convince consumers that certain objects have cultural value, I emphasis the dialogue between merchants and consumers, and their mutual negotiation of cultural meaning through objects.