How different cultures react and respond given a crisis is predominant in a society's norms and political will to combat the situation. Often, the decisions made are necessitated by events, social pressure, or the need of the hour, which may not represent the nation's will. While some are pleased with it, others might show resentment. Coronavirus (COVID-19) brought a mix of similar emotions from the nations towards the decisions taken by their respective governments. Social media was bombarded with posts containing both positive and negative sentiments on the COVID-19, pandemic, lockdown, and hashtags past couple of months. Despite geographically close, many neighboring countries reacted differently to one another. For instance, Denmark and Sweden, which share many similarities, stood poles apart on the decision taken by their respective governments. Yet, their nation's support was mostly unanimous, unlike the South Asian neighboring countries where people showed a lot of anxiety and resentment. The purpose of this study is to analyze reaction of citizens from different cultures to the novel Coronavirus and people's sentiment about subsequent actions taken by different countries. Deep long short-term memory (LSTM) models used for estimating the sentiment polarity and emotions from extracted tweets have been trained to achieve stateof-the-art accuracy on the sentiment140 dataset. The use of emoticons showed a unique and novel way of validating the supervised deep learning models on tweets extracted from Twitter.
Urdu is still considered a low-resource language despite being ranked as the world's 10 th most spoken language with nearly 230 million speakers. The scarcity of benchmark datasets in lowresource languages has led researchers to utilize more ingenious techniques to curb the issue. One such option widely adopted is to use language translation services to replicate existing datasets from resourcerich languages such as English to low-resource languages, such as Urdu. For most natural language processing tasks, including polarity assessment, words translated via Google translator from one language to another often change the meaning. It results in a polarity shift causing the system's performance degradation, particularly for sentiment classification and emotion detection tasks. This study evaluates the effect of translation on the sentiment classification task from a resource-rich language to a low-resource language. It identifies and enlists words causing polarity shift into five distinct categories. It further finds the correlation between the language with similar roots. Our study shows 2-3 percentage points performance degradation due to polarity shift as a result of translation from resource-rich languages to low-resource languages.
It has been more than a year since the coronavirus (COVID-19) engulfed the whole world, disturbing the daily routine, bringing down the economies, and killing two million people across the globe at the time of writing. The pandemic brought the world together to a joint effort to find a cure and work toward developing a vaccine. Much to the anticipation, the first batch of vaccines started rolling out by the end of 2020, and many countries began the vaccination drive early on while others still waiting in anticipation for a successful trial. Social media, meanwhile, was bombarded with all sorts of both positive and negative stories of the development and the evolving coronavirus situation. Many people were looking forward to the vaccines, while others were cautious about the side-effects and the conspiracy theories resulting in mixed emotions. This study explores users’ tweets concerning the COVID-19 vaccine and the sentiments expressed on Twitter. It tries to evaluate the polarity trend and a shift since the start of the coronavirus to the vaccination drive across six countries. The findings suggest that people of neighboring countries have shown quite a similar attitude regarding the vaccination in contrast to their different reactions to the coronavirus outbreak.
This article describes how the enormous potential benefits provided by the cloud services, made enterprises to show huge interest in adopting cloud computing. As the service provider has control over the entire data of an organization stored onto the cloud, a malicious activity, whether internal or external can tamper with the data and computation. This causes enterprises to lack trust in adopting services due to privacy, security and trust issues. Despite of having such issues, the consumer has no root level access right to secure and check the integrity of procured resources. To establish a trust between the consumer and the provider, it is desirable to let the consumer to check the procured platform hosted at provider side for safety and security. This article proposes an architectural design of a trusted platform for the IaaS cloud computing by the means of which the consumer can check the integrity of a guest platform. TCG's TPM is deployed and used on the consumer side as the core component of the proposed architecture and it is distributed between the service provider and the consumer.
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