Word embedding enrichment for dictionary construction: An example of incivility in Cantonese

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.