Business applications of unstructured text

Kuechler, William L.

doi:10.1145/1290958.1290967

Cited by 28 publications

(17 citation statements)

References 4 publications

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“…LSA has emerged in an information retrieval context but was subsequently followed by works focusing on discourse processing, document comparisons, document categorization and quantification of textual data as a preprocessing step in predictive analytics (Kundu et al, 2015;Evangelopoulos, 2013;Sidorova, Evangelopoulos, Valacich, & Ramakrishnan, 2008;Coussement et al, 2008). Kuechler (2007) outlined several business and information systems applications that could potentially benefit from LSA of textual data (Table 1).…”

Section: Using Latent Semantic Analysis (Lsa) To Examine Textual Datamentioning

confidence: 99%

A business intelligence approach using web search tools and online data reduction techniques to examine the value of product-enabled services

Tanev

Liotta

Kleismantas

2015

Expert Systems with Applications

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Section: Using Latent Semantic Analysis (Lsa) To Examine Textual Datamentioning

confidence: 99%

A business intelligence approach using web search tools and online data reduction techniques to examine the value of product-enabled services

Tanev

Liotta

Kleismantas

2015

Expert Systems with Applications

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“…Velocity refers to the speed with which data can be collected and analysed, with real-time analysis becoming a possibility with even very large data sets. Variety is significant as it signals a shift away from simply collecting data in text form towards data in video, audio and image formats (Kuechler, 2007). More recently, big data has moved beyond its technical roots to encompass a broad range of commercial opportunities enabled through the analysis of such data (Manyika et al, 2011).…”

Section: Introducing Big Datamentioning

confidence: 99%

Big Data: A Normal Accident Waiting to Happen?

Nunan

Domenico

2015

J Bus Ethics

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Widespread commercial use of the internet has significantly increased the volume and scope of data being collected by organisations. 'Big data' has emerged as a term to encapsulate both the technical and commercial aspects of this growing data collection activity. To date, much of the discussion of big data has centred upon its transformational potential for innovation and efficiency, yet there has been less reflection on its wider implications beyond commercial value creation. This paper builds upon normal accident theory (NAT) to analyse the broader ethical implications of big data. It argues that the strategies behind big data require organisational systems that leave them vulnerable to normal accidents, that is to say some form of accident or disaster that is both unanticipated and inevitable. Whilst NAT has previously focused on the consequences of physical accidents, this paper suggests a new form of system accident that we label data accidents. These have distinct, less tangible and more complex characteristics and raise significant questions over the role of individual privacy in a 'data society'. The paper concludes by considering the ways in which the risks of such data accidents might be managed or mitigated.

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“…Studies indicate that, from 2005 to 2020, the digital universe will grow from 130 exabytes to 40000 exabytes (40 trillion gigabytes) [9]. According to [10], about 80% of these data are in unstructured format, and a major part of this percentage is formed by textual documents. The internet plays an important role in these data, since its content is created and accessed by billions of worldwide people.…”

Section: Introductionmentioning

confidence: 99%

Named entities as privileged information for hierarchical text clustering

Sinoara

Sundermann

Marcacini

et al. 2014

Proceedings of the 18th International Database Engineering &Amp; Applications Symposium on - IDEAS '14

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Text clustering is a text mining task which is often used to aid the organization, knowledge extraction, and exploratory search of text collections. Nowadays, the automatic text clustering becomes essential as the volume and variety of digital text documents increase, either in social networks and the Web or inside organizations. This paper explores the use of named entities as privileged information in a hierarchical clustering process, so as to improve clusters quality and interpretation. We carried out an experimental evaluation on three text collections (one written in Portuguese and two written in English) and the results show that named entities can be applied as privileged information to power clustering solution in dynamic text collection scenarios.

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Business applications of unstructured text

Abstract: Crunching unstructured text rather than numbers is the basis for a new class of high-value applications ranging from regulatory compliance monitoring to business intelligence.

Cited by 28 publications

References 4 publications

A business intelligence approach using web search tools and online data reduction techniques to examine the value of product-enabled services

A business intelligence approach using web search tools and online data reduction techniques to examine the value of product-enabled services

Big Data: A Normal Accident Waiting to Happen?

Named entities as privileged information for hierarchical text clustering

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