Analyzing COVID-2019 Impact on Mental Health Through Social Media Forum

This study aims to identify the potential association of mental health and social media forum during the outbreak of COVID-19 pandemic.COVID-19 brings a lot of challenges to government globally.Among the different strategies the most extensively adopted ones were lockdown,social distancing,and isolation among others.Most people with no mental illness history have been found with high risk of distress and psychological discomfort due to anxiety of being infected with the virus.Panic among people due to COVID-19 spread faster than the disease itself.The misinformation and excessive usage of social media in this pandemic era have adversely affected mental health across the world.Due to limited historical data,psychiatrists are finding it difficult to cure the mental illness of people resulting from the pandemic repercussion,fueled by social media forum.In this study the methodology used for data extraction is by considering the implications of social network platforms(such as Reddit)and levering the capabilities of a semi-supervised co-training technique-based use of Naïve Bayes(NB),Random Forest(RF),and Support Vector Machine(SVM)classifiers.The experimental results shows the efficacy of the proposed methodology to identify the mental illness level(such as anxiety,bipolar disorder,depression,PTSD,schizophrenia,and OCD)of those who are in anxious of being infected with this virus.We observed 1 to 5%improvement in the classification decision through the proposed method as compared to state-of-the-art classifiers.

Analyzing COVID-19 Impact on the Researchers Productivity through Their Perceptions

Context:Since the end of 2019,the COVID-19 pandemic had a worst impact on world’s economy,healthcare,and education.There are several aspects where the impact of COVID-19 could be visualized.Among these,one aspect is the productivity of researcher,which plays a significant role in the success of an organization.Problem:There are several factors that could be aligned with the researcher’s productivity of each domain and whose analysis through researcher’s feedback could be beneficial for decision makers in terms of their decision making and implementation of mitigation plans for the success of an organization.Method:We perform an empirical study to investigate the substantial impact of COVID-19 on the productivity of researchers by analyzing the relevant factors through their perceptions.Our study aims to find out the impact of COVID-19 on the researcher’s productivity that are working in different fields.In this study,we conduct a questionnaire-based analysis,which included feedback of 152 researchers of certain domains.These researchers are currently involved in different research activities.Subsequently,we perform a statistical analysis to analyze the collected responses and report the findings.Findings:The results indicate the substantial impact of COVID-19 pandemics on the researcher’s productivity in terms of mental disturbance,lack of regular meetings,and field visits for the collection of primary data.Conclusion:Finally,it is concluded that researcher’s daily or weekly meetings with their supervisors and colleagues are necessary to keep them more productive in task completion.These findings would help the decision makers of an organization in the settlement of their plan for the success of an organization.

Timed-Automata Based Model-Checking of a Multi-Agent System: A Case Study

A multi-agent based transport system is modeled by timed automata model extended with clock variables. The correctness properties of safety and liveness of this model are verified by timed automata based UPPAAL. Agents have a degree of control on their own actions, have their own threads of control, and under some circumstances they are also able to take decisions. Therefore they are autonomous. The multi-agent system is modeled as a network of timed automata based agents supported by clock variables. The representation of agent requirements based on mathematics is helpful in precise and unambiguous specifications, thereby ensuring correctness. This formal representation of requirements provides a way for logical reasoning about the artifacts produced. We can be systematic and precise in assessing correctness by rigorously specifying the functional requirements.

A systematic study on the role of SentiWordNet in opinion mining

Sentiment lexicons(SL)(aka lexical resources)are the repositories of one or several dictionaries that consist of known and precompiled sentiment terms.These lexicons play an important role in performing several different opinion mining tasks.The efficacy of the lexicon-based approaches in performing opinion mining(OM)tasks solely depends on selecting an appropriate opinion lexicon to analyze the text.Therefore,one has to explore the available sentiment lexicons and then select the most suitable resource.Among available resources,SentiWordNet(SWN)is the most widely used lexicon to perform tasks related to opinion mining.In SWN,each synset of WordNet is being assigned the three sentiment numerical scores;positive,negative and objective that are calculated using by a set of classifiers.In this paper,a detailed and comprehensive review of the work related to opinion mining using Senti-WordNet is provided in a very distinctive way.This survey will be useful for the researchers contributing to the field of opinion mining.Following features make our contribution worthwhile and unique among the reviews of similar kind:(i)our review classifies the existing literature with respect to opinion mining tasks and subtasks(ii)it covers a very different outlook of the opinion mining field by providing in-depth discussions of the existing works at different granularity levels(word,sentences,document,aspect,clause,and concept levels)(iii)this state-ofart review covers each article in the following dimensions:the designated task performed,granularity level of the task completed,results obtained,and feature dimensions,and(iv)lastly it concludes the summary of the related articles according to the granularity levels,publishing years,related tasks(or subtasks),and types of classifiers used.In the end,major challenges and tasks related to lexicon-based approaches towards opinion mining are also discussed.