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Generalized word shift graphs: a method for visualizing and explaining pairwise comparisons between texts
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In: Springer Berlin Heidelberg (2021)
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Abstract:
Abstract A common task in computational text analyses is to quantify how two corpora differ according to a measurement like word frequency, sentiment, or information content. However, collapsing the texts’ rich stories into a single number is often conceptually perilous, and it is difficult to confidently interpret interesting or unexpected textual patterns without looming concerns about data artifacts or measurement validity. To better capture fine-grained differences between texts, we introduce generalized word shift graphs, visualizations which yield a meaningful and interpretable summary of how individual words contribute to the variation between two texts for any measure that can be formulated as a weighted average. We show that this framework naturally encompasses many of the most commonly used approaches for comparing texts, including relative frequencies, dictionary scores, and entropy-based measures like the Kullback–Leibler and Jensen–Shannon divergences. Through a diverse set of case studies ranging from presidential speeches to tweets posted in urban green spaces, we demonstrate how generalized word shift graphs can be flexibly applied across domains for diagnostic investigation, hypothesis generation, and substantive interpretation. By providing a detailed lens into textual shifts between corpora, generalized word shift graphs help computational social scientists, digital humanists, and other text analysis practitioners fashion more robust scientific narratives.
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URL: https://hdl.handle.net/1721.1/131952
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| 2 |
Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter
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In: Sci Adv (2021)
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How the world’s collective attention is being paid to a pandemic: COVID-19 related n-gram time series for 24 languages on Twitter
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In: PLoS One (2021)
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Storywrangler: A massive exploratorium for sociolinguistic, cultural, socioeconomic, and political timelines using Twitter ...
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Fame and Ultrafame: Measuring and comparing daily levels of `being talked about' for United States' presidents, their rivals, God, countries, and K-pop ...
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Divergent discourse between protests and counter-protests: #BlackLivesMatter and #AllLivesMatter ...
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Sentiment analysis methods for understanding large-scale texts: a case for using continuum-scored words and word shift graphs ...
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Sentiment analysis methods for understanding large-scale texts: a case for using continuum-scored words and word shift graphs ...
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Forecasting the onset and course of mental illness with Twitter data
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| 12 |
The Lexicocalorimeter: Gauging public health through caloric input and output on social media
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| 13 |
Forecasting the onset and course of mental illness with Twitter data
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| 14 |
Forecasting the onset and course of mental illness with Twitter data ...
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| 15 |
The Lexicocalorimeter: Gauging public health through caloric input and output on social media
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In: PLoS (2015)
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Benchmarking sentiment analysis methods for large-scale texts: A case for using continuum-scored words and word shift graphs ...
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