Sentiment analysis of COVID-19 cases in Greece using Twitter data.

Syndromic surveillance with the use of Internet data has been used to track and forecast epidemics for the last two decades, using different sources from social media to search engine records. More recently, studies have addressed how the World Wide Web could be used as a valuable source for analysing the reactions of the public to outbreaks and revealing emotions and sentiment impact from certain events, notably that of pandemics. Objective: The objective of this research is to evaluate the capability of Twitter messages (tweets) in estimating the sentiment impact of COVID-19 cases in Greece in real time as related to cases. Methods: 153,528 tweets were gathered from 18,730 Twitter users totalling 2,840,024 words for exactly one year and were examined towards two sentimental lexicons: one in English language translated into Greek (using the Vader library) and one in Greek. We then used the specific sentimental ranking included in these lexicons to track i) the positive and negative impact of COVID-19 and ii) six types of sentiments: Surprise, Disgust, Anger, Happiness, Fear and Sadness and iii) the correlations between real cases of COVID-19 and sentiments and correlations between sentiments and the volume of data. Results: Surprise (25.32%) mainly and secondly Disgust (19.88%) were found to be the prevailing sentiments of COVID-19. The correlation coefficient (R2 ) for the Vader lexicon is &#8722; 0.07454 related to cases and &#8722; 0.,70668 to the tweets, while the other lexicon had 0.167387 and &#8722; 0.93095 respectively, all measured at significance level of p < 0.01. Evidence shows that the sentiment does not correlate with the spread of COVID-19, possibly since the interest in COVID-19 declined after a certain time

EXTENDING IMS COURSE STRUCTURES FOR CONDITIONAL LEARNING PATH SUPPORT

Abstract. Current LMS standards do not explicitly address the encoding of the metadata structures that are needed to support student learning-paths in user-adaptative web-based learning systems. We describe our approach in extending the IMS content packaging standards to include metadata for knowledge-based learning path navigation, along with insights in how to exploit that information in intelligent navigation or agent-based content delivery systems

Towards a Unified Query-By-Example (UQBE): UML as a basis

A generic graphical query language for ODMG-compliant object databases is proposed, based on the ideas of Query-By-Example, and using UML-like diagrams as schema notation. Ease of learning for users coming from the relational world and support for non object-oriented data sources are also considered as design goals. The overall layout of the query language is described, illustrating its potential expressive power via ODMG OQL comparisons