ABSTRACT
We have yet to realise the full capability of social media as an innovative information platform during emergencies and crisis response and management. Sentiment analysis can systematically identify, extract, and scrutinise emotional states and subjective information in social media data. Exploring reactions and perceptions to response messaging is invaluable and proved especially useful for a pandemic response as it can demonstrate general population reaction to the pandemic and governments response actions. This can be further analysed to identify the gap between government response actions and communications and citizens' perceptions. In this paper, an analysis of Twitter data explores population reaction towards COVID-19 health messaging. A Natural Language Processing Python tool is known as TextBlob was used to discover general data sentiment. Data were divided into three sentiments and text extraction of health messages was conducted to explore subsequent tweets in predefined categories. Our findings show the outcome of Tweets analysis could help us to identify the general population concerns and their reactions to COVID-19 to give a better understanding of the situation to governments and support them in implementing appropriate policies. © 2021 IEEE.
ABSTRACT
Organisations that have survived Covid-19 jolts, uncertainties and lockdowns now require business intelligence and information system integration for decision support. Risks and market challenges have shifted as the pandemic progresses. Crisis management repositioning has left many firms cash-poor. Now, firms are facing labour shortages. Standard remuneration policies and practices need revision to keep core talent, stabilise teams from burn-out and to move forward strategically. This paper revises essential remuneration review considerations for crisis management in a new Covid-19 context. Current trends are discussed, as restrictions and pandemic uncertainties lift, allowing greater accuracy in risk scenario planning. Reliable data, fit for analytics and complex modelling, are making results more meaningful. The unique contribution of this research is the Covid-19 post-survival organisational perspective that can transform remuneration modelling. The scope extends beyond the governance level, and considers semantics and dynamic risk planning. Specific scenarios are updated from lessons learned from financial crisis management. Conclusions take a holistic view of how data can enhance remuneration practices to add value for organisations in the current Covid-19 climate. The paper advocates post crisis remuneration review that includes complex modelling with dynamic risk analysis for strategic planning in mid-term scenarios. © 2021 IEEE.
ABSTRACT
According to the World Health Organization (WHO), about 91% of the global populace live in locations with air quality levels below WHO guidelines. Although ambient air pollution impacts both developed and developing nations, low-and middle-income nations suffer the highest consequences with the highest toll experienced in the Western Pacific and South East Asia regions. Poor air quality results in odour types ranging from chemical combustion smells to fungal smells. Fungal odour is formed due to damp indoor conditions like high humidity and temperature. This damp condition produces microscopic fungi which are allergens (substances that cause allergic reactions), irritants and toxic substances referred to as mould. Inhaling or touching mould spores may cause allergic reactions like sneezing, runny nose, redness of the eyes and skin rash and even respiratory complications. In this era of a global pandemic, having a general-purpose odour detection system becomes imperative. This research, therefore, aims to design and implement an odour detection system that can alert individuals or allergic sufferers to high content of toxic gases in their surroundings or homes. The study is a valuable resource for users to measure their air quality and to support those with respiratory vulnerabilities, especially in the Covid-19 environment of today. © 2020 IEEE.