Covid 19 has seen the world go into a lock down and unconventional social situations throughout. During this time, the world saw a surge in information sharing around the pandemic and the topics shared in the time were diverse. People’s sentiments have changed during this period. Given the wide spread usage of Online Social Networks (OSN) and support groups, the user sentiment is well reflected in online discussions. In this work, we aim to show the topics under discussion, evolution of discussions, change in user sentiment during the pandemic. Alongside which, we also demonstrate the possibility of exploratory analysis to find pressing topics, change in perception towards the topics and ways to use the knowledge extracted from online discussions. For our work we employ Diachronic Word embeddings which capture the change in word usage over time. With the help of analysis from temporal word usages, we show the change in people’s option on covid-19 from being a conspiracy, to the post-covid topics that surround vaccination.

In this paper, we propose an annotation scheme which can be used not only for annotating coreference relations between linguistic expressions, but also those among linguistic expressions and images, in scientific texts such as biomedical articles. Images in biomedical domain often contain important information for analyses and diagnoses, and we consider that linking images to textual descriptions of their semantic contents in terms of coreference relations is useful for multimodal access to the information. We present our annotation scheme and the concept of a "coreference pool," which plays a central role in the scheme. We also introduce a support tool for text annotation named Open Ontology Forge which we have already developed, and additional functions for the software to cover image annotations (ImageOF) which is now being developed.