What is Text Analysis, Really?

Rockwell, Geoffrey

doi:10.1093/llc/18.2.209

Cited by 51 publications

(16 citation statements)

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“…For a volume to be fit for fulltext processing, it must support one or more examples of data processing, including image processing and text extraction (OCR), linguistic analysis, automated translation, and other forms of natural language processing (Rockwell 2003), most typically applied in the digital humanities.…”

Section: Use-case Scenariosmentioning

confidence: 99%

Archival quality and long-term preservation: a research framework for validating the usefulness of digital surrogates

Conway

2011

Arch Sci

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Digital archives accept and preserve digital content for long-term use. Increasingly, stakeholders are creating large-scale digital repositories to ingest surrogates of archival resources or digitized books whose intellectual value as surrogates may exceed that of the original sources themselves. Although digital repository developers have expended significant effort to establish the trustworthiness of repository procedures and infrastructures, relatively little attention has been paid to the quality and usefulness of the preserved content itself. In situations where digital content has been created by third-party firms, content quality (or its absence in the form of unacceptable error) may directly influence repository trustworthiness. This article establishes a conceptual foundation for the association of archival quality and information quality research. It outlines a research project that is designed to develop and test measures of quality for digital content preserved in HathiTrust, a large-scale preservation repository. The research establishes methods of measuring error in digitized books at the data, page, and volume level and applies the measures to statistically valid samples of digitized books, adjusting for inter-coder inconsistencies and the effects of sampling strategies. The research findings are then validated with users who conform to one of four use-case scenarios: reading online, printing on demand, data mining, and print collection management. The paper concludes with comments on the implications of assessing archival quality within a digital preservation context.

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Section: Use-case Scenariosmentioning

confidence: 99%

Archival quality and long-term preservation: a research framework for validating the usefulness of digital surrogates

Conway

2011

Arch Sci

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“…The previous section demonstrated WordSeer's capabilities as an exploratory analysis environment through an investigation of gender portrayals in Shakespeare. By comparison, this analysis would be impossible or extremely time-consuming in other tools developed for humanities scholars such as MONK (Unsworth and Mueller, 2009) and Voyant (Rockwell, 2003;Rockwell et al, 2010). For example, in either of these systems, searching the plays for items possessed by women would entail keyword search with her, and guesses such as eyes, face, and beauty.…”

Section: Discussionmentioning

confidence: 99%

“…The second is Voyant (Rockwell, 2003;Rockwell et al, 2010), which operates entirely at the word level. It allows users to plot word frequencies, see concordances (contexts in which words occur) and create tag clouds.…”

Section: Related Systemsmentioning

confidence: 99%

Supporting exploratory text analysis in literature study

Muralidharan

Hearst

2012

Literary and Linguistic Computing

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We present WordSeer, an exploratory analysis environment for literary text. Literature study is a cycle of reading, interpretation, exploration, and understanding. While there is now abundant technological support for reading and interpreting literary text in new ways through text-processing algorithms, the other parts of the cycle-exploration and understanding-have been relatively neglected. We are motivated by the literature on sensemaking, an area of computer science devoted to supporting open-ended analysis on large collections of data. Our software system integrates tools for algorithmic processing of text with interaction techniques that support the interpretive, exploratory, and note-taking aspects of scholarship. At present, the system supports grammatical search and contextual similarity determination, visualization of patterns of word context, and examination and organization of the source material for comparison and hypothesis building. This article illustrates its capabilities by analyzing language-use differences between male and female characters in Shakespeare's plays. We find that when love is a major plot point, the language Shakespeare uses to refer to women becomes more physical, and the language referring to men becomes more sentimental. Future work will incorporate additional sensemaking tools to aid comparison, exploration, grouping, and pattern recognition.

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“…With the development of computational approaches; however, critics and researchers have come to think about how effective computational approaches are in identifying meanings within texts. Now, it is often assumed that computational approaches prove effective in better understanding texts in question [15]. This is best described as a process of decoding meanings within texts [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Feature Selection in Text Clustering Applications of Literary Texts: A Hybrid of Term Weighting Methods

Omar¹

2020

IJACSA

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The recent years have witnessed an increasing use of automated text clustering approaches and more particularly Vector Space Clustering (VSC) methods in the computational analysis of literary data including genre classification, theme analysis, stylometry, and authorship attribution. In spite of the effectiveness of VSC methods in resolving different problems in these disciplines and providing evidence-based research findings, the problem of feature selection remains a challenging one. For reliable text clustering applications, a clustering structure should be based on only and all the most distinctive features within a corpus. Although different term weighting approaches have been developed, the problem of identifying the most distinctive variables within a corpus remains challenging especially in the document clustering applications of literary texts. For this purpose, this study proposes a hybrid of statistical measures including variance analysis, term frequency-inverse document frequency, TF-IDF, and Principal Component Analysis (PCA) for selecting only and all the most distinctive features that can be usefully used for generating more reliable document clustering that can be usefully used in authorship attribution tasks. The study is based on a corpus of 74 novels written by 18 novelists representing different literary traditions. Results indicate that the proposed model proved effective in the successful extraction of the most distinctive features within the datasets and thus generating reliable clustering structures that can be usefully used in different computational applications of literary texts.

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What is Text Analysis, Really?

Cited by 51 publications

References 10 publications

Archival quality and long-term preservation: a research framework for validating the usefulness of digital surrogates

Archival quality and long-term preservation: a research framework for validating the usefulness of digital surrogates

Supporting exploratory text analysis in literature study

Feature Selection in Text Clustering Applications of Literary Texts: A Hybrid of Term Weighting Methods

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