Interactive Visual Analytics for Sensemaking with Big Text

Dowling, Michelle; Wycoff, Nathan; Mayer, Brian; Wenskovitch, John; Leman, Scotland; House, Leanna; Polys, Nicholas F.; North, Chris; Hauck, Peter

doi:10.1016/j.bdr.2019.04.003

Cited by 18 publications

(19 citation statements)

References 13 publications

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“…Another study [49] managed to narrow the 1,25 million results for one keyword to 131 759 blog posts for all keywords by using a specialized search engine. When key terms do not occur simultaneously, arranging terms under topics can help to find relevant documents [56,62]. Although a lot of research on online health communities (OHC) has used a manual content analysis approach, the approach becomes quickly unfeasible when the volume increases.…”

Section: Finding Relevant Datamentioning

confidence: 99%

Qualitative Big Data’s Challenges and Solutions: An Organizing Review

Suvivuo¹

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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Digitalization of everyday lives has tremendously increased the amount of digital (trace) data of people's behaviour available for researchers. However, traditional qualitative research methods struggle with the width and breadth of the data. This paper reviewed 61 recent studies that had utilized qualitative big data for the practical challenges they had encountered and how they were addressed. While quantitative and qualitative big data share many common issues, the review points at that lack of qualitative methods and dataset reduction required by algorithms in big data research decreases the richness of the qualitative data. Locating relevant data and reducing noise are further challenges. Currently, these challenges can be only partially addressed with a combination of human and computer pattern recognition and crowdsourcing. The review describes many "tricks of the trade" but abduction research and pragmatist philosophy seem promising starting places for a more pervasive framework.

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Section: Finding Relevant Datamentioning

confidence: 99%

Qualitative Big Data’s Challenges and Solutions: An Organizing Review

Suvivuo¹

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…SI's ability to infer the analyst's reasoning process is fundamentally limited by the feature space of the underlying machine learning. Text analytics systems with SI, such as ForceSPIRE [4,12] and Cosmos [9], typically use keywords (such as text terms and phrases) as data features (SI keyword in Fig. 1a), known as bag-ofwords [31].…”

Section: Semantic Interaction With Word Embeddingsmentioning

confidence: 99%

Evaluating Semantic Interaction on Word Embeddings via Simulation

Bian

Dowling

North

2020

Preprint

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Semantic interaction (SI) attempts to learn the user's cognitive intents as they directly manipulate data projections during sensemaking activity. For text analysis, prior implementations of SI have used common data features, such as bag-of-words representations, for machine learning from user interactions. Instead, we hypothesize that features derived from deep learning word embeddings will enable SI to better capture the user's subtle intents. However, evaluating these effects is difficult. SI systems are usually evaluated by a human-centred qualitative approach, by observing the utility and effectiveness of the application for end-users. This approach has drawbacks in terms of replicability, scalability, and objectiveness, which makes it hard to perform convincing contrast experiments between different SI models. To tackle this problem, we explore a quantitative algorithm-centered analysis as a complementary evaluation approach, by simulating users' interactions and calculating the accuracy of the learned model. We use these methods to compare word-embeddings to bag-of-words features for SI.

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“…Quantitative tools such as Dis-Function [11] present similarity-based projections of the data, with a user interest weight vector applied to the dimensions of the dataset to influence the projection in response to user interactions. These learning techniques also support text data, as seen in systems like StarSPIRE [9,82] and Cosmos [24]. One key change for the text case is creating a similarity computation based on a distance metric such as Cosine distance to resolve issues with term sparcity [78] or Gower distance to adjust for missing terms [37].…”

Section: Dimension Reduction and Clustering Toolsmentioning

confidence: 99%

An Examination of Grouping and Spatial Organization Tasks for High-Dimensional Data Exploration

Wenskovitch¹,

North²

2020

Preprint

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How do analysts think about grouping and spatial operations? This overarching question incorporates a number of points for investigation, including understanding how analysts begin to explore a dataset, the types of grouping/spatial structures created and the operations performed on them, the relationship between grouping and spatial structures, the decisions analysts make when exploring individual observations, and the role of external information. This work contributes the design and results of such a study, in which a group of participants are asked to organize the data contained within an unfamiliar quantitative dataset. We identify several overarching approaches taken by participants to design their organizational space, discuss the interactions performed by the participants, and propose design recommendations to improve the usability of future high-dimensional data exploration tools that make use of grouping (clustering) and spatial (dimension reduction) operations.

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Interactive Visual Analytics for Sensemaking with Big Text

Cited by 18 publications

References 13 publications

Qualitative Big Data’s Challenges and Solutions: An Organizing Review

Qualitative Big Data’s Challenges and Solutions: An Organizing Review

Evaluating Semantic Interaction on Word Embeddings via Simulation

An Examination of Grouping and Spatial Organization Tasks for High-Dimensional Data Exploration

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