Data Analytics in Digital Humanities

Hai-Jew, Shalin

doi:10.1007/978-3-319-54499-1

Cited by 12 publications

(7 citation statements)

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“…We used the Meaning Extraction Helper software (MEH; Boyd, 2017) to analyze these corpora. The MEH software was used to individually quantify the prevalence of each WIP dictionary word using a standard word-counting approach (see Boyd, 2017;Pennebaker, Booth, et al, 2015). In addition to WIP dictionary words, we separately quantified other common words by using the same approach.…”

Section: Resultsmentioning

confidence: 99%

“…This methodological approach increases the probability that agentic or communal words included in the sentence refer to the subject whose descriptions we would like to evaluate (e.g., professions in Study 3). Nevertheless, our experience is that such incremental methodological refinements usually do not change the main results (see Boyd, 2017).…”

Section: Limitationsmentioning

confidence: 99%

“…The recent accessibility of large quantities of natural language data has prompted the development of computerized applications for text analysis (Tausczik & Pennebaker, 2010). Such methods of linguistic inquiry rely on the assumption that language is inevitably linked to human psychology and that psychological processes find their reflection in the words that people use, often unconsciously (Boyd, 2017;Dzogang, Lightman, & Cristianini, 2018).…”

Section: Word Counting Approach In Language Analysismentioning

confidence: 99%

“…These dictionaries represent a complement to the Linguistic Inquiry and Word Count (LIWC; Pennebaker, Booth, Boyd, & Francis, 2015) dictionary, which is currently one of the most prominent tools for quantifying psychological content in natural language (see Tausczik & Pennebaker, 2010). The LIWC framework is particularly well suited for the quantification of psychological phenomena from language samples (e.g., Boyd, 2017). LIWC dictionaries have been widely validated and successfully used in various psychological studies (see Tausczik & Pennebaker, 2010), for instance, to predict extraversion from oral/written utterances (Mehl, Gosling, & Pennebaker, 2006;Pennebaker & King, 1999).…”

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confidence: 99%

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The big two dictionaries: Capturing agency and communion in natural language

Pietraszkiewicz

Formanowicz

Sendén

et al. 2019

Euro J Social Psych

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Four studies developed and validated two dictionaries to capture agentic and communal expressions in natural language. Their development followed the Linguistic Inquiry and Word Count (LIWC) approach (Study 1) and we tested their validity with frequency‐based analyses and semantic similarity measures. The newly developed Agency and Communion dictionaries were aligned with LIWC categories related to agency and communion (Study 2), and corresponded with subjective ratings (Study 3), confirming their convergent validity. Very low or absent correspondence between proposed dictionaries and unrelated LIWC categories demonstrated their discriminant validity (Study 2). Finally, we applied both dictionaries to language used in advertisements. In correspondence to gender stereotypes, male‐dominated jobs were advertised with more agentic than communal words, and female‐dominated jobs with more communal than agentic words (Study 4). Both dictionaries represent reliable tools for quantifying agentic and communal content in natural language, and will improve and facilitate future research on agency and communion.

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Section: Resultsmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Section: Word Counting Approach In Language Analysismentioning

confidence: 99%

mentioning

confidence: 99%

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The big two dictionaries: Capturing agency and communion in natural language

Pietraszkiewicz

Formanowicz

Sendén

et al. 2019

Euro J Social Psych

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“…For an AI system to be able to perceive engaged contexts to assess whether the description of an act, or instruction, is fair, a fairness metric by which it can measure such activity is required. Currently, metrics to assess human qualities such as sentiment and personality have been well validated in the literature (Boyd et al, 2015;Hai-Jew 2017;Youyou et al 2015). However, a valid and reliable measure of fairness has yet to be developed.…”

Section: Introductionmentioning

confidence: 99%

Word vector embeddings hold social ontological relations capable of reflecting meaningful fairness assessments

Izzidien

2021

AI & Soc

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Programming artificial intelligence (AI) to make fairness assessments of texts through top-down rules, bottom-up training, or hybrid approaches, has presented the challenge of defining cross-cultural fairness. In this paper a simple method is presented which uses vectors to discover if a verb is unfair (e.g., slur, insult) or fair (e.g., thank, appreciate). It uses already existing relational social ontologies inherent in Word Embeddings and thus requires no training. The plausibility of the approach rests on two premises. That individuals consider fair acts those that they would be willing to accept if done to themselves. Secondly, that such a construal is ontologically reflected in Word Embeddings, by virtue of their ability to reflect the dimensions of such a perception. These dimensions being: responsibility vs. irresponsibility, gain vs. loss, reward vs. sanction, joy vs. pain, all as a single vector (FairVec). The paper finds it possible to quantify and qualify a verb as fair or unfair by calculating the cosine similarity of the said verb’s embedding vector against FairVec—which represents the above dimensions. We apply this to Glove and Word2Vec embeddings. Testing on a list of verbs produces an F1 score of 95.7, which is improved to 97.0. Lastly, a demonstration of the method’s applicability to sentence measurement is carried out.

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Deepwater Archaeological Survey: An Interdisciplinary and Complex Process

Drap

Papini

Merad

et al. 2019

3D Recording and Interpretation for Maritime Archaeology

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This chapter introduces several state of the art techniques that could help to make deep underwater archaeological photogrammetric surveys easier, faster, more accurate, and to provide more visually appealing representations in 2D and 3D for both experts and public. We detail how the 3D captured data is analysed and then represented using ontologies, and how this facilitates interdisciplinary interpretation and cooperation. Towards more automation, we present a new method that adopts a deep learning approach for the detection and the recognition of objects of interest, amphorae for example. In order to provide more readable, direct and clearer illustrations, we describe several techniques that generate different styles of sketches out of orthophotos developed using neural networks. In the same direction, we present the Non-Photorealistic Rendering (NPR) technique, which converts a 3D model into a more readable 2D representation that is more useful to communicate and simplifies the identification of objects of interest. Regarding public dissemination, we demonstrate how recent advances in virtual reality to provide an accurate, high resolution, amusing and appropriate visualization tool that offers the public the possibility to 'visit' an unreachable archaeological site. Finally, we conclude by introducing the plenoptic approach, a new promising technology that can change the future of the photogrammetry by making it easier and less time consuming and that allows a user to create a 3D model using only one camera shot. Here, we introduce the concepts, the developing process, and some results, which we obtained with underwater imaging.

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Data Analytics in Digital Humanities

Cited by 12 publications

References 0 publications

The big two dictionaries: Capturing agency and communion in natural language

The big two dictionaries: Capturing agency and communion in natural language

Word vector embeddings hold social ontological relations capable of reflecting meaningful fairness assessments

Deepwater Archaeological Survey: An Interdisciplinary and Complex Process

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