Detecting Geographic Associations in English Dialect Features in North America within a Visual Data Mining Environment Integrating Self‐Organizing Maps

Thill, Jean‐Claude; Kretzschmar, William A.; Casas, Irene; Yao, Xiaobai

doi:10.1002/9780470021699.ch5

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…Nerbonne (2006) introduced factor analysis to aggregate linguistic analysis. Thill et al (2008) adopted Kohonen's (2001) self-organizing map to analyze the variations of word usage and pronunciation using the LAMSAS dataset. Principal component analysis (PCA) is another popular method used for multivariate analysis, which reduces variable dimensions with fewer measurements while retaining data variability in the original data (Rao 1964).…”

Section: Introductionmentioning

confidence: 99%

Understanding U.S. regional linguistic variation with Twitter data analysis

Huang

Guo

Kasakoff

et al. 2016

Computers, Environment and Urban Systems

131

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We analyze a Big Data set of geo-tagged tweets for a year (Oct. 2013-Oct. 2014) to understand the regional linguistic variation in the U.S. Prior work on regional linguistic variations usually took a long time to collect data and focused on either rural or urban areas. Geo-tagged Twitter data offers an unprecedented database with rich linguistic representation of fine spatiotemporal resolution and continuity. From the one-year Twitter corpus, we extract lexical characteristics for twitter users by summarizing the frequencies of a set of lexical alternations that each user has used. We spatially aggregate and smooth each lexical characteristic to derive county-based linguistic variables, from which orthogonal dimensions are extracted using the principal component analysis (PCA). Finally a regionalization method is used to discover hierarchical dialect regions with using the PCA components. The regionalization results reveal interesting linguistic regional variations in the U.S. The discovered regions not only confirm past research findings in the literature but also provide new insights and a more detailed understanding of very recent linguistic patterns in the U.S.

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Section: Introductionmentioning

confidence: 99%

Understanding U.S. regional linguistic variation with Twitter data analysis

Huang

Guo

Kasakoff

et al. 2016

Computers, Environment and Urban Systems

131

View full text Add to dashboard Cite

show abstract

“…Jack Grieve (2014a, 2014b) has also applied MDS to American dialect data to make estimates of regions. Kretzschmar and Thill developed the use of neural networks and self-organizing maps (SOM) to investigate aggregated data (Thill et al 2008). This procedure created numerous sets of weighted collections of features that could be associated with particular American survey speakers and thus make a collection of dialect maps.…”

Section: Visualizing Regions In Languagementioning

confidence: 99%

Creation of Regions for Dialect Features Using a Cellular Automaton

Juuso

Kretzschmar

2016

Journal of English Linguistics

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An issue in dialect research has been how to make generalizations from survey data about where some dialect feature might be found. Precomputational methods included drawing isoglosses or using shadings to indicate areas where an analyst expected a feature to be found. The use of computers allowed for faster plotting of locations where any given feature had been elicited, and also allowed for the use of statistical techniques from technical geography to estimate regions where particular features might be found. However, using the computer did not make the analysis less subjective than isoglosses, and statistical methods from technical geography have turned out to be limited in use. We have prepared a cellular automaton (CA) for use with data collected for the Linguistic Atlas Project that can address the problems involved in this type of data visualization. The CA plots the locations where survey data were elicited, and then through the application of rules creates an estimate of the spatial distributions of selected features. The application of simple rules allows the CA to create objective and reproducible estimates based on the data it was given, without the use of statistical methods.A continuous issue from the beginnings of dialect research has been how to make a reasonable generalization from survey data about where some dialect feature might be found in a survey area. Precomputational methods included drawing isoglosses or

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“…Himanen et al (1998) explore the applicability of SOMs in identifying archetypical daily travel patterns. Thill et al (2007) use an SOM to analyze spatial and nonspatial patterns of usage of English words in North American regions of the Atlantic seaboard. A number of studies have used SOMs to organize demographic and housing data gathered from censuses and surveys to investigate patterns of change in urban and regional systems (Hatzichristos, 2004;Kauko, 2005;Koua and Kraak, 2004a;2004b;Openshaw et al, 1995;Skupin and Hagelman, 2005).…”

Section: Self-organizing Mapsmentioning

confidence: 99%

Visual Data Mining in Spatial Interaction Analysis with Self-Organizing Maps

Yan

Thill

2009

Environ Plann B Plann Des

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Given that many spatial interaction (SI) systems are often constituted in large databases with high thematic dimensionality, data complexity reduction tasks are essential. The opportunity exists for researchers to examine the formation of different types of SIs as well as their interdependencies by exploring the patterns embedded in the data. To circumvent the limitations of existing methods of flow data compression and visual exploration, we propose an integrated computational and visual approach, known as VISIDAMIN, for handling both SI data projection and SI data quantization at once. The computational method of self-organizing maps serves as the data mining engine in this process. Using a large domestic air travel dataset as a case study, we examine how the characteristics of the air transport system interact with the SI system to create relationships and structures within the US domestic airline market.

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Detecting Geographic Associations in English Dialect Features in North America within a Visual Data Mining Environment Integrating Self‐Organizing Maps

Cited by 5 publications

References 14 publications

Understanding U.S. regional linguistic variation with Twitter data analysis

Understanding U.S. regional linguistic variation with Twitter data analysis

Creation of Regions for Dialect Features Using a Cellular Automaton

Visual Data Mining in Spatial Interaction Analysis with Self-Organizing Maps

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