11 research outputs found

    Word Embedding Enrichment for Dictionary Construction: An Example of Incivility in Cantonese

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    Dictionary-based methods remain valuable to measure concepts based on texts, though supervised machine learning has been widely used in much recent communication research. The present study proposes a semi-automatic and easily implemented method to build and enrich dictionaries based on word embeddings. As an example, we create a dictionary of political incivility that contains vulgarity and name-calling words in Cantonese. The study shows that dictionary-based classification outperforms supervised machine learning methods, including deep neural network models. Furthermore, a small number of random seed words can generate a highly accurate dictionary. However, the uncivil content detected is only weakly correlated with uncivil perceptions, as we demonstrate in a population-based survey experiment. The strengths and limitations of dictionary-based methods are discussed

    A unified approach to exploratory factor analysis with missing data, nonnormal data, and in the presence of outliers

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    simple structure, standard errors, test statistics, equivariance, invariance, nonnormal data, outliers, missing data,
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