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.

Every picture tells a story: using selfie-inspired activities to enhance social relations and encourage self-reflexivity.

This chapter explores the results of a study in Thailand that capitalised on the popularity of the selfie, providing second-year English language students with an opportunity to practise their oral presentation and speaking skills. The selfie was used not in the usual sense of online picture-sharing, but as a visual aid in a face-to-face interaction, thus serving as a “currency for social interaction” (van Dijck 2008, p.62) and communication device (Saltz, 2014). Mining the rich insights gained from the Thai study, this chapter presents another selfie-inspired activity adapted for a different context and purpose at a UK university. Initially designed to facilitate recall of students’ names linked with faces, the initiative evolved into an effective conversation starter. It is suggested that both selfie-inspired initiatives have led to serendipitous results, such as encouraging self-reflexivity among the students and promoting the development of “rapid intimacy” in the classroom (Victoria 2011, p.72). Indeed, creating a space for students to share their personal stories and enact different identities can help enrich the learning and teaching experience. This chapter also demonstrates how aspects of visual methodologies can be employed as a resource for theorising visual data, such as the selfie, for classroom application.

Semantic Mining based on graph theory and ontologies. Case Study: Cell Signaling Pathways

In this paper we use concepts from graph theory and cellular biology represented as ontologies, to carry out semantic mining tasks on signaling pathway networks. Specifically, the paper describes the semantic enrichment of signaling pathway networks. A cell signaling network describes the basic cellular activities and their interactions. The main contribution of this paper is in the signaling pathway research area, it proposes a new technique to analyze and understand how changes in these networks may affect the transmission and flow of information, which produce diseases such as cancer and diabetes. Our approach is based on three concepts from graph theory (modularity, clustering and centrality) frequently used on social networks analysis. Our approach consists into two phases: the first uses the graph theory concepts to determine the cellular groups in the network, which we will call them communities; the second uses ontologies for the semantic enrichment of the cellular communities. The measures used from the graph theory allow us to determine the set of cells that are close (for example, in a disease), and the main cells in each community. We analyze our approach in two cases: TGF-β and the Alzheimer Disease.

VOX POPULI: THREE ESSAYS ON THE USE OF SOCIAL MEDIA FOR VALUE CREATION IN SERVICES

Prior research shows that electronic word of mouth (eWOM) wields considerable influence over consumer behavior. However, as the volume and variety of eWOM grows, firms are faced with challenges in analyzing and responding to this information. In this dissertation, I argue that to meet the new challenges and opportunities posed by the expansion of eWOM and to more accurately measure its impacts on firms and consumers, we need to revisit our methodologies for extracting insights from eWOM. This dissertation consists of three essays that further our understanding of the value of social media analytics, especially with respect to eWOM. In the first essay, I use machine learning techniques to extract semantic structure from online reviews. These semantic dimensions describe the experiences of consumers in the service industry more accurately than traditional numerical variables. To demonstrate the value of these dimensions, I show that they can be used to substantially improve the accuracy of econometric models of firm survival. In the second essay, I explore the effects on eWOM of online deals, such as those offered by Groupon, the value of which to both consumers and merchants is controversial. Through a combination of Bayesian econometric models and controlled lab experiments, I examine the conditions under which online deals affect online reviews and provide strategies to mitigate the potential negative eWOM effects resulting from online deals. In the third essay, I focus on how eWOM can be incorporated into efforts to reduce foodborne illness, a major public health concern. I demonstrate how machine learning techniques can be used to monitor hygiene in restaurants through crowd-sourced online reviews. I am able to identify instances of moral hazard within the hygiene inspection scheme used in New York City by leveraging a dictionary specifically crafted for this purpose. To the extent that online reviews provide some visibility into the hygiene practices of restaurants, I show how losses from information asymmetry may be partially mitigated in this context. Taken together, this dissertation contributes by revisiting and refining the use of eWOM in the service sector through a combination of machine learning and econometric methodologies.

Preserving and interpreting the mining company office : landscape, space and technological change in the management of the copper industry

The purpose of this research is to examine the role of the mining company office in the management of the copper industry in Michigan’s Keweenaw Peninsula between 1901 and 1946. Two of the largest and most influential companies were examined – the Calumet & Hecla Mining Company and the Quincy Mining Company. Both companies operated for more than forty years under general managers who were arguably the most influential people in the management of each company. James MacNaughton, general manager at Calumet and Hecla, worked from 1901 through 1941; Charles Lawton, general manager at Quincy Mining Company, worked from 1905 through 1946. In this case, both of these managers were college-educated engineers and adopted scientific management techniques to operate their respective companies. This research focused on two main goals. The first goal of this project was to address the managerial changes in Michigan’s copper mining offices of the early twentieth century. This included the work of MacNaughton and Lawton, along with analysis of the office structures themselves and what changes occurred through time. The second goal of the project was to create a prototype virtual exhibit for use at the Quincy Mining Company office. A virtual exhibit will allow visitors the opportunity to visit the office virtually, experiencing the office as an office worker would have in the early twentieth century. To meet both goals, this project used various research materials, including archival sources, oral histories, and material culture to recreate the history of mining company management in the Copper Country.

Technology, mining methods and landscapes of a placer mining district in Fairbanks, Alaska, 1900-1930

Placer miners in Alaska’s interior were part of the last great gold rush in North America. As word of gold in the Fairbanks Mining District traveled down the Yukon River, a wave of miners from the Klondike placer fields in Dawson, along with a assortment of speculators and inexperienced green horns from the Lower 48 converged on the confluence of the Tanana and Chena rivers hoping to strike it rich. The steamers coming from Dawson were integral; they carried miners with experience working the frozen subarctic placer deposits of the Klondike. These miners encountered new environmental challenges that required the development of new technologies and mining methods to efficiently harvest gold. These methods and machines were brought into Fairbanks and further perfected to account for the local conditions. This thesis describes the local mining technologies and methods employed in the Fairbanks district and the landscape patterns created during the placer mining boom years of 1903-1909, decline years of 1910-1923 and recovery of 1923-1930.

More favorable combination of circumstances could hardly have been desired : a bottom to top examination of the Pittsburgh and Boston Mining Company's Cliff Mine

The Cliff Mine, an archaeological site situated on the Keweenaw Peninsula of Michigan, is the location of the first successful attempt to mine native copper in North America. Under the management of the Pittsburgh & Boston Mining Company from 1845-1879, two-third of the Cliff’s mineral output was in the form of mass copper, some pieces of which weighed over 5 tons when removed from the ground. The unique nature of mass copper and the Cliff Mine’s handling of it make it one of the best examples of early mining processes in the Keweenaw District. Mass copper only constituted 2% of the entire product of the Lake Superior copper districts, and the story of early mining on the Peninsula is generally overshadowed by later, longer running mines such as the Calumet & Helca and Quincy Mining Companies. Operating into the mid-twentieth century, the size and duration of these later mines would come to define the region, though they would not have been possible without the Cliff’s early success. Research on the Cliff Mine has previously focused on social and popular history, neglecting the structural remains. However, these remains are physical clues to the technical processes that defined early mining on the Keweenaw. Through archaeological investigations, these processes and their associated networks were documented as part of the 2010 Michigan Technological Archaeology Field School’s curriculum. The project will create a visual representation of these processes utilizing Geographic Information Systems software. This map will be a useful aid in future research, community engagement and possible future interpretive planning.

Challenging masculinity in CSR disclosures:silencing of women’s voices in Tanzania’s mining industry

This paper presents a feminist analysis of corporate social responsibility (CSR) in a male-dominated industry within a developing country context. It seeks to raise awareness of the silencing of women’s voices in CSR reports produced by mining companies in Tanzania. Tanzania is one of the poorest countries in Africa, and women are often marginalised in employment and social policy considerations. Drawing on work by Hélène Cixous, a post-structuralist/radical feminist scholar, the paper challenges the masculinity of CSR discourses that have repeatedly masked the voices and concerns of ‘other’ marginalised social groups, notably women. Using interpretative ethnographic case studies, the paper provides much-needed empirical evidence to show how gender imbalances remain prevalent in the Tanzanian mining sector. This evidence draws attention to the dynamics faced by many women working in or living around mining areas in Tanzania. The paper argues that CSR, a discourse enmeshed with the patriarchal logic of the contemporary capitalist system, is entangled with tensions, class conflicts and struggles which need to be unpacked and acknowledged. The paper considers the possibility of policy reforms in order to promote gender balance in the Tanzanian mining sector and create a platform for women’s concerns to be voiced.

The effect of gender context on children's social behaviour in a limited resources situation : an observational study

Knowing when to compete and when to cooperate to maximize opportunities for equal access to activities and materials in groups is critical to children's social and cognitive development. The present study examined the individual (gender, social competence) and contextual factors (gender context) that may determine why some children are more successful than others. One hundred and fifty-six children (M age=6.5 years) were divided into 39 groups of four and videotaped while engaged in a task that required them to cooperate in order to view cartoons. Children within all groups were unfamiliar to one another. Groups varied in gender composition (all girls, all boys, or mixed-sex) and social competence (high vs. low). Group composition by gender interaction effects were found. Girls were most successful at gaining viewing time in same-sex groups, and least successful in mixed-sex groups. Conversely, boys were least successful in same-sex groups and most successful in mixed-sex groups. Similar results were also found at the group level of analysis; however, the way in which the resources were distributed differed as a function of group type. Same-sex girl groups were inequitable but efficient whereas same-sex boy groups were more equitable than mixed groups but inefficient compared to same-sex girl groups. Social competence did not influence children's behavior. The findings from the present study highlight the effect of gender context on cooperation and competition and the relevance of adopting an unfamiliar peer paradigm when investigating children's social behavior.