In today's world, the social media is everywhere, and everybody come in contact with it every day. With social media datas, we are able to do a lot of analysis and statistics nowdays. Within this scope of article, we conclude and analyse the sentiments and manifestations (comments, hastags, posts, tweets) of the users of the Twitter social media platform, based on the main trends (by keyword, which is mostly the 'covid' and coronavirus theme in this article) with Natural Language Processing and with Sentiment Classification using Recurrent Neural Network. Where we analyse, compile, visualize statistics, and summarize for further processing. The trained model works much more accurately, with a smaller margin of error, in determining emotional polarity in today's 'modern' often with ambiguous tweets. Especially with RNN. We use this fresh scraped data collections (by the keyword's theme) with our RNN model what we have created and trained to determine what emotional manifestations occurred on a given topic in a given time interval.

Indoor navigation is an important research topic nowadays. The complexity of larger buildings, supermarkets, museums, etc. makes it necessary to use applications which can facilitate the orientation. While for outdoor navigation already exist tried and tested solutions, but few reliable ones are available for indoor navigation. In this paper we investigate the possible technologies for indoor navigation. Then, we present a general, cost effective system as a solution. This system uses the advantages of semantic web to store data and to compute the possible paths as well. Furthermore it uses Augmented Reality techniques and map view to provide interaction with the users. We made a prototype based on client-server architecture. The server runs in a cloud and provides the appropriate data to the client, which can be a smartphone or a tablet with Android operation system.

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