Topic modeling is commonly used to analyze and understand large document collections. However, in practice, users want to focus on specific aspects or "targets" rather than the entire corpus. For example, given a large collection of documents, users may want only a smaller subset which more closely aligns with their interests, tasks, and domains. In particular, our paper focuses on large-scale document retrieval with high recall where any missed relevant documents can be critical. A simple keyword matching search is generally not effective nor efficient as 1) it is difficult to find a list of keyword queries that can cover the documents of interest before exploring the dataset, 2) some documents may not contain the exact keywords of interest but may still be highly relevant, and 3) some words have multiple meanings, which would result in irrelevant documents included in the retrieved subset. In this paper, we present TopicSifter, a visual analytics system for interactive search space reduction. Our system utilizes targeted topic modeling based on nonnegative matrix factorization and allows users to give relevance feedback in order to refine their target and guide the topic modeling to the most relevant results.
Various information visualization techniques have been applied to improve user interfaces for search. Some systems augment search result lists with additional small visualizations. For example, TileBars @cite_26 , INSYDER @cite_5 , and HotMap @cite_25 visualize query-document relationships as icons or glyphs alongside search results. Another approach is to visualize search results in a spatial layout where proximity represents similarity. Systems such as InfoSky @cite_20 and IN-SPIRE @cite_19 are examples. FacetAtlas @cite_3 overlays additional heatmaps to visualize density. ProjSnippet @cite_34 visualizes text snippets in a 2-D layout. Many others cluster the search results and offer faceted navigation. FacetMap @cite_4 and ResultMap @cite_27 utilizes treemap-style visualizations to represent facets. These systems may guide users well in exploring search results, but they are mostly based on static search queries. Our system goes beyond search results exploration and offers interactive target (query) building.
@cite_26 @cite_4 @cite_3 @cite_19 @cite_27 @cite_5 @cite_34 @cite_25 @cite_20
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The field of information retrieval has traditionally focused on textbases consisting of titles and abstracts. As a consequence, many underlying assumptions must be altered for retrieval from full-length text collections. This paper argues for making use of text structure when retrieving from full text documents, and presents a visualization paradigm, called TileBars, that demonstrates the usefulness of explicit term distribution information in Boolean-type queries. TileBars simultaneously and compactly indicate relative document length, query term frequency, and query term distribution. The patterns in a column of TileBars can be quickly scanned and deciphered, aiding users in making judgments about the potential relevance of the retrieved documents. The dominant paradigm for searching and browsing large data stores is text-based: presenting a scrollable list of search results in response to textual search term input. While this works well for the Web, there is opportunity for improvement in the domain of personal information stores, which tend to have more heterogeneous data and richer metadata. In this paper, we introduce FacetMap, an interactive, query-driven visualization, generalizable to a wide range of metadata-rich data stores. FacetMap uses a visual metaphor for both input (selection of metadata facets as filters) and output. Results of a user study provide insight into tradeoffs between FacetMap's graphical approach and the traditional text-oriented approach Documents in rich text corpora usually contain multiple facets of information. For example, an article about a specific disease often consists of different facets such as symptom, treatment, cause, diagnosis, prognosis, and prevention. Thus, documents may have different relations based on different facets. Powerful search tools have been developed to help users locate lists of individual documents that are most related to specific keywords. However, there is a lack of effective analysis tools that reveal the multifaceted relations of documents within or cross the document clusters. In this paper, we present FacetAtlas, a multifaceted visualization technique for visually analyzing rich text corpora. FacetAtlas combines search technology with advanced visual analytical tools to convey both global and local patterns simultaneously. We describe several unique aspects of FacetAtlas, including (1) node cliques and multifaceted edges, (2) an optimized density map, and (3) automated opacity pattern enhancement for highlighting visual patterns, (4) interactive context switch between facets. In addition, we demonstrate the power of FacetAtlas through a case study that targets patient education in the health care domain. Our evaluation shows the benefits of this work, especially in support of complex multifaceted data analysis. Professional analysts deal with a high volume of information and must constantly work to separate out the valuable data. However, analysts have difficulty determining what data is useful without reading or skimming almost all returned documents from a search. This presents them with a difficult tradeoff. Searching information broadly returns hundreds or thousands of documents. We present lessons learned from an observational study of the application of the InSpire visually oriented text exploitation system in an operational analysis environment. Hierarchical representations are common in digital repositories, yet are not always fully leveraged in their online search interfaces. This work describes ResultMaps, which use hierarchical treemap representations with query string-driven digital library search engines. We describe two lab experiments, which find that ResultsMap users yield significantly better results over a control condition on some subjective measures, and we find evidence that ResultMaps have ancillary benefits via increased understanding of some aspects of repository content. The ResultMap system and experiments contribute an understanding of the benefits-direct and indirect-of the ResultMap approach to repository search visualization. This paper presents INSYDER, a content-based visual-information-seeking system for the Web. The Web can be seen as one huge digital library offering a variety of very useful information for business analysts. INSYDER addresses these possibilities and offers powerful retrieval and visualisation functionalities. The main focus during the development was on the usability of the system. Therefore, a variety of well-established visualisation components were employed to support the user during the information-seeking process (e.g. visual query, result table, bar graph, segment view with tile bars, and scatterplot). Also, the retrieval aspects were developed with the goal of increasing the usability of the system (e.g. natural language search, content-based classification, relevance feedback). Extensive evaluations of the retrieval performance and the usability of the visualisation were conducted. The results of these evaluations offered many helpful insights into developing a new visual-information-seeking system called VisMeB. Users of traditional web search engines commonly find it difficult to evaluate the results of their web searches. We suggest the use of information visualization and interactive visual manipulation as methods for improving the ability of users to evaluate the results of a web search. In this paper, we present the results of a user study that compared the search results interface provided by Google to that of two systems we have developed: HotMap and Concept Highlighter. We found that users were able to perform their searches faster with HotMap, were able to find more relevant documents with Concept Highlighter, and generally ranked these interfaces higher than Google with respect to subjective measures. When given a choice between these interfaces, participants ranked HotMap the highest, followed by Google and Concept Highlighter. These results indicate that even though the list-based representation of search results are common among search engines, visual and interactive interfaces to web search results can be more efficient, effective, and satisfying to the users. InfoSky is a system enabling users to explore large, hierarchically structured document collections. Similar to a real-world telescope, InfoSky employs a planar graphical representation with variable magnification. Documents of similar content are placed close to each other and are visualised as stars, forming clusters with distinct shapes. For greater performance, the hierarchical structure is exploited and force-directed placement is applied recursively at each level on much fewer objects, rather than on the whole corpus. Collections of documents at a particular level in the hierarchy are visualised with bounding polygons using a modified weighted Voronoi diagram. Their area is related to the number of documents contained. Textual labels are displayed dynamically during navigation, adjusting to the visualisation content. Navigation is animated and provides a seamless zooming transition between summary and detail view. Users can map metadata such as document size or age to attributes of the visualisation such as colour and luminance. Queries can be made and matching documents or collections are highlighted. Formative usability testing is ongoing; a small baseline experiment comparing the telescope browser to a tree browser is discussed.