Thematic Analysis: A Corpus-Based Method for Understanding Themes/Topics of a Corpus through a Classification Process Using Long Short-Term Memory (LSTM)

Using advanced algorithms to conduct a thematic analysis reduces the time taken and increases the efficiency of the analysis. Long short-term memory (LSTM) is effective in the field of text classification and natural language processing (NLP). In this study, we adopt LSTM for text classification in order to perform a thematic analysis using concordance lines that are taken from a corpora of news articles. However, the statistical and quantitative analyses of corpus linguistics are not enough to fully identify the semantic shift of terms and concepts. Therefore, we suggest that a corpus should be classified from a linguistic theoretical perspective, as this would help to determine the level of the linguistic patterns that should be applied in the experiment of the classification process. We suggest investigating the concordance lines of the articles rather than only the relationship between collocates, as this has been a limitation for many studies. The findings of this research work highlight the effectiveness of the proposed methodology for the thematic analysis of media coverage, reaching 84% accuracy. This method provides a deeper thematic analysis than only applying the classification process through the collocational analysis.

Impact of the loss of smell on the sleep quality and fatigue level in COVID­19 survivors.

PURPOSE: Patients with Coronavirus disease 2019 (COVID-19) are reported to have symptoms such as shortness of breath, dry cough, headache, fatigue, and diarrhea. Loss of smell is a symptom that some patients have suffered from due to inflammation of olfactory epithelium and neuroinvasion of COVID-19 resulting in damage to the olfactory nerves and olfactory bulb. Losing an important sense such as smell might have unfavorable consequences on the lives of COVID-19 survivors; however, these unfavorable consequences have not been sufficiently investigated. METHODS: This was a cross-sectional descriptive study, 81 COVID-19 survivors (51.85% male) answered the Pittsburgh Sleep Quality Index, Fatigue Severity Scale, and Patient Health Questionnaire. RESULTS: COVID-19 survivors who lost their smell were more likely to have poor sleep quality, high fatigue severity, and depression symptoms compared to others who did not lose their smell. Most COVID-19 survivors who lost their smell were women and had breathing difficulties. CONCLUSION: Our knowledge of this relationship will assist in establishing more efficient treatment regimens that consider both psychological and physiological factors. Future research is needed to investigate the causality relationship between poor sleep quality, increased fatigue, and depression symptoms in COVID-19 survivors who experienced loss of the sense of smell.