To prevent the public from pandemic Covid’19 the government of India has started the vaccination from mid of January 2021. The government has approved the two vaccines, Covishield from the university of Oxford and Covaxin from Bharat Biotech.The vaccination started with frontline workers and is further extended to common public prioritizing the elders of above 60 years and people aged 45 years above with co morbidities. Though many people have got benefitted from it there is still a group of people not convinced with the vaccination. We have carried out this work to analyze those Indian people sentiments on the vaccines through the hash tags of tweets. The results show that though majority of the community has a positive belief on the vaccines but some of them still express negative emotions.

Previous studies on supporting free- form keyword queries over RDBMSs provide users with linked-structures (e.g., a set of joined tuples) that are relevant to a given keyword query. Most of them focus on ranking individual tuples from one table or joins of multiple tables containing a set of keywords. The problem of keyword search in a data cube with text-rich dimension(s) (so-called text cube) is studied. The text cube is built on a multidimensional text database, where each row is associated with some text data (a document) and other structural dimensions (attributes). A cell in the text cube aggregates a set of documents with matching attribute values in a subset of dimensions. Given a keyword query, the goal is to find the top-k most relevant cells. This project studies the problem of keyword-based top k search in text cube, i.e., given a keyword query, find the top-k most relevant cells in a text cube. When users want to retrieve information from a text cube using keyword queries, relevant cells, rather than relevant documents, are preferred as the answers, because:(i) relevant cells are easy for users to browse; and (ii)relevant cells provide users insights about the relationship between the values of relational attributes and the text data. The proposed algorithm uses relevance scoring formula for finding the top-k relevant cells by exploring only a small portion of the whole text cube (when k is small) and enables early terminatio.

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The entire world is affected because of the global pandemic Covid-19 due to the virus belongs to the family of Coronavirus. As the spread of infection and mortality rate is rapid people have started developing assorted emotions about the crisis. It is more significant to administer the mental health and Psychological wellbeing of public during a crisis like this. As many of the people broadly use the social media like twitter for sharing their opinions and thoughts, our work utilizes the Covid specific Tweets posted by the Tweeple and analyse them to understand the sentiments exhibited regarding the situation. After the tweets are collected and the real sentiments behind them are discovered using the classifier model developed using the Machine Learning methods. The experimental results may be used by the respective authorities to take necessary initiatives for addressing the concerns that affect the wellbeing of the society and the economic wellbeing as well. As our word uses Lexical based sentiment analysis, it is important to remove the ambiguities of a word which is a main challenge of this technique on sentiment analysis. To improve the performance of the Sentiment Analysis we have used the lexical dictionaries Wordnet and SentiWordNet along with Word Sense Disambiguation (WSD) to detect and remove the ambiguities understanding the context of the term used in the tweets.