No abstract

With developments of modern and advanced information and communication technologies (ICTs), Industry 4.0 has launched big data analysis, natural language processing (NLP), and artificial intelligence (AI). Corpus analysis is also a part of big data analysis. For many cases of statistic-based corpus techniques adopted to analyze English for specific purposes (ESP), researchers extracted critical information by retrieving domain-oriented lexical units. However, even if corpus software embraces algorithms such as log-likelihood tests, log ratios, BIC scores, etc., the machine still cannot understand linguistic meanings. In many ESP cases, function words reduce the efficiency of corpus analysis. However, many studies still use manual approaches to eliminate function words. Manual annotation is inefficient and time-wasting, and can easily cause information distortion. To enhance the efficiency of big textual data analysis, this paper proposes a novel statistic-based corpus machine processing approach to refine big textual data. Furthermore, this paper uses COVID-19 news reports as a simulation example of big textual data and applies it to verify the efficacy of the machine optimizing process. The refined resulting data shows that the proposed approach is able to rapidly remove function and meaningless words by machine processing and provide decision-makers with domain-specific corpus data for further purposes.

Purpose: Knowledge, attitude, and practice (KAP) models are often used by researchers in the field of public health to explore people’s healthy behaviors. Therefore, this study mainly explored the relationships among participants’ sociodemographic status, COVID-19 knowledge, affective attitudes, and preventive behaviors. Method: This study adopted an online survey, involving a total of 136 males and 204 females, and used a cross-sectional study to investigate the relationships between variables including gender, age, COVID-19 knowledge, positive affective attitudes (emotional wellbeing, psychological wellbeing, and social wellbeing), negative affective attitudes (negative self-perception and negative perceptions of life), and preventive behaviors (hygiene habits, reducing public activities, and helping others to prevent the epidemic). Results: The majority of participants in the study were knowledgeable about COVID-19. The mean COVID-19 knowledge score was 12.86 (SD = 1.34, range: 7–15 with a full score of 15), indicating a high level of knowledge. However, the key to decide whether participants adopt COVID-19 preventive behaviors was mainly their affective attitudes, especially positive affective attitudes (β = 0.18–0.25, p< 0.01), rather than COVID-19 disease knowledge (β = −0.01–0.08, p > 0.05). In addition, the sociodemographic status of the participants revealed obvious differences in the preventive behaviors; females had better preventive behaviors than males such as cooperating with the epidemic prevention hygiene habits (t = −5.08, p< 0.01), reducing public activities (t = −3.00, p< 0.01), and helping others to prevent the epidemic (t = −1.97, p< 0.05), while the older participants were more inclined to adopt preventive behaviors including epidemic prevention hygiene habits (β = 0.18, p = 0.001, R2 = 0.03), reducing public activities (β = 0.35, p< 0.001, R2 = 0.13), and helping others to prevent the epidemic (β = 0.27, p< 0.001, R2 = 0.07). Conclusions: Having adequate COVID-19 knowledge was not linked to higher involvement in precautionary behaviors. Attitudes toward COVID-19 may play a more critical function in prompting individuals to undertake preventive behaviors, and different positive affective attitudes had different predictive relationships with preventive behaviors.