Text mining

Blake, Catherine

doi:10.1002/aris.2011.1440450110

Cited by 30 publications

(27 citation statements)

References 114 publications

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“…Text mining serves to "identify […] patterns from a collection of texts" [74]. By using computers and natural language processing methods, it is possible to handle large amounts of unstructured text data.…”

Section: General Introduction To Text Miningmentioning

confidence: 99%

“…After the first step, selection of a sample (i), the text data require "pre-processing" to remove "noise" (ii) [74,76], e.g., by "stemming" words or deleting irrelevant "stopwords" such as articles or prepositions [74,75]. Then, "features to represent the data" are selected [76], and a "dimensionality reduction" is performed to increase information density (iii) [74,76]. The most common text representation with a reasonable performance is splitting text into a vector of terms [74,77].…”

Section: General Introduction To Text Miningmentioning

confidence: 99%

“…Then, "features to represent the data" are selected [76], and a "dimensionality reduction" is performed to increase information density (iii) [74,76]. The most common text representation with a reasonable performance is splitting text into a vector of terms [74,77]. This results in a document-term-matrix (d-t-m).…”

Section: General Introduction To Text Miningmentioning

confidence: 99%

“…Depending on the chosen representation, various methods for dimensionality reduction may be applied. In case of a d-t-m, this may include "pruning terms based on their frequency," "principal component analysis," or mapping synonymous terms "onto a higher order representation" [74]. After data structuring, data analysis is performed (iv).…”

Section: General Introduction To Text Miningmentioning

confidence: 99%

“…Whether previous knowledge is considered in the steps of text representation and analysis makes a major difference: A pure machine learning approach relies on probabilistic models, whereas a knowledge-based approach involves "existing knowledge resources" such as pre-defined ontologies or training data [74]. In this study, the latter approach was followed.…”

Section: General Introduction To Text Miningmentioning

confidence: 99%

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A new approach to semantic sustainability assessment: text mining via network analysis revealing transition patterns in German municipal climate action plans

Bickel

2017

Energ Sustain Soc

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Background: Various monitoring approaches have shown that urban areas and their energy systems are major contributors to climate change. Corresponding observations have mostly been based on physical data. However, text data is an untapped source of information that can be analyzed by text mining methods. Taking the example of the German Energy Transition, an interpretation network analysis was used to assess local transition patterns in 16 municipal climate action plans of regional centers in the State of Lower Saxony. Based on the holistic concept of social-ecological systems, three analytical perspectives were introduced, the social system, the energy system, and the three principles of strong sustainability, which inspired three questions: What is the "horizon of attention" regarding the stages of the energy system? What potential, in terms of coordination or collaboration, can be identified on basis of network links between societal sub-systems and the energy system? Are strong sustainability principles adequately linked to the energy system to support a transition towards sustainability? Methods: A computer-aided interpretation network analysis was used. The (co-)occurrence of indicative words representing pre-defined categories was checked in the measures proposed in the plans to analyze the importance and connectedness of these categories. For this purpose, three thesauri were created as fixed literature-based categorization schemes.

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Section: General Introduction To Text Miningmentioning

confidence: 99%

Section: General Introduction To Text Miningmentioning

confidence: 99%

Section: General Introduction To Text Miningmentioning

confidence: 99%

Section: General Introduction To Text Miningmentioning

confidence: 99%

Section: General Introduction To Text Miningmentioning

confidence: 99%

See 3 more Smart Citations

A new approach to semantic sustainability assessment: text mining via network analysis revealing transition patterns in German municipal climate action plans

Bickel

2017

Energ Sustain Soc

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Defining textual entailment

Korman

Mack²,

Jett

et al. 2018

Asso for Info Science & Tech

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Textual entailment is a relationship that obtains between fragments of text when one fragment in some sense implies the other fragment. The automation of textual entailment recognition supports a wide variety of textbased tasks, including information retrieval, information extraction, question answering, text summarization, and machine translation. Much ingenuity has been devoted to developing algorithms for identifying textual entailments, but relatively little to saying what textual entailment actually is. This article is a review of the logical and philosophical issues involved in providing an adequate definition of textual entailment. We show that many natural definitions of textual entailment are refuted by counterexamples, including the most widely cited definition of Dagan et al. We then articulate and defend the following revised definition: T textually entails H 5 df typically, a human reading T would be justified in inferring the proposition expressed by H from the proposition expressed by T. We also show that textual entailment is context-sensitive, nontransitive, and nonmonotonic.

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Understanding characteristics of semantic associations in health consumer generated knowledge representation in social media

Park

2019

Asso for Info Science & Tech

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This study explores knowledge organization behavior on the Web with respect to identifying the semantic relationships of health-related concepts. In particular, this study aims to investigate the potentials of imparting richer collective intelligence to existing knowledge representation systems in health. The study focuses on detecting semantic relationships between semantic groups of major concepts mined from health consumers' descriptions of health issues and associated user-generated metadata (i.e., tags). A total of 50,263 blogs and associated 341,720 tags were collected from Tumblr, a blogging social networking site. Text mining and semantic network analysis methods were used to explore the usage patterns at semantic type levels of the identified medical concepts in tags, in blogs, and between tags and blogs. More various associations among semantic types were identified both in tags and in blogs. These associations were more diverse and complicated than the relationships in the Unified Medical Language System Semantic Network. Among the groups of concepts in tags and blogs, groups showed relatively stronger and more diverse relationships with other groups of concepts. In addition, many direct and close relations were found between tags and blogs.

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Text mining

Cited by 30 publications

References 114 publications

A new approach to semantic sustainability assessment: text mining via network analysis revealing transition patterns in German municipal climate action plans

A new approach to semantic sustainability assessment: text mining via network analysis revealing transition patterns in German municipal climate action plans

Defining textual entailment

Understanding characteristics of semantic associations in health consumer generated knowledge representation in social media

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