RESUMEN
Recent studies on the COVID-19 pandemic indicated an increase in the level of anxiety, stress, and depression among people of all ages. The World Health Organization (WHO) recently warned that even with the approval of vaccines by the Food and Drug Administration (FDA), population immunity is highly unlikely to be achieved this year. This paper aims to analyze people's sentiments during the pandemic by combining sentiment analysis and natural language processing algorithms to classify texts and extract the polarity, emotion, or consensus on COVID-19 vaccines based on tweets. The method used is based on the collection of tweets under the hashtag #COVIDVaccine while the nltk toolkit parses the texts, and the tf-idf algorithm generates the keywords. Both n-gram keywords and hashtags mentioned in the tweets are collected and counted. The results indicate that the sentiments are divided into positive and negative emotions, with the negative ones dominating.
RESUMEN
Recent statistical and social studies have shown that social media platforms such as Instagram, Facebook, and Twitter contain valuable data that influence human behaviors. This data can be used to track, fight, and control the spread of the COVID-19 and are an excellent asset for analyzing and understanding people's sentiments. Current levels of willingness to receive a COVID-19 vaccination are still insufficient to achieve immunity standards as stipulated by the World Health Organization (WHO). The present study employs bibliometric analysis to uncover trends and research into sentiment analysis and COVID-19 vaccination. A range of analyses is conducted using the open-source tool VOSviewer and Scopus database from 2020-2021 to acquire a deeper insight and evaluate current research trends on COVID-19 vaccines. The quantitative methodology used generates various bibliometric network visualizations and trends as a function of publication metrics such as citation, geographical attributes, journal publications, and research institutions. Results of network visualization revealed that understanding the the-state-of-the-art in applying sentiment analysis to the COVID-19 pandemic is crucial to local government health agencies and healthcare providers to help in neutralizing the infodemic and improve vaccine acceptance.
RESUMEN
The Hamilton cycle problem is closely related to a series of famous problems and puzzles (traveling salesman problem, Icosian game) and, due to the fact that it is NP-complete, it was extensively studied with different algorithms to solve it. The most efficient algorithm is not known. In this paper, a necessary condition for an arbitrary un-directed graph to have Hamilton cycle is proposed. Based on this condition, a mathematical solution for this problem is developed and several proofs and an algorithmic approach are introduced. The algorithm is successfully implemented on many Hamiltonian and non-Hamiltonian graphs. This provides a new effective approach to solve a problem that is fundamental in graph theory and can influence the manner in which the existing applications are used and improved.