Covid-19 had consequential social, economic, and extreme mental outcomes on the community, where media platforms like Twitter increasingly became essential networking mediums generating information with a large volume of reports, views, opinions, and information shared by individuals and authorized outlets. In 2021, when the second wave of COVID emerged in India, we recognised the fastest outbreak with more than 20 lakh cases in April’s first half. Until then, India distributed over one billion vaccine units with two producers: Bharat Biotech, producing Covaxin, and Covishield, OxfordAstraZeneca’s vaccine, by SII (Serum Institute of India). We collected datasets for analysis, and applied our novel algorithms for preprocessing, i.e., removal of URLs, @, #, contracted words, punctuations, numbers, POS, etc. Converted tweets into tokenized words, used stemming & lemmatizations, then applied neural spellchecker. Using our in-house algorithm, we cleaned around 500 tweets in just 0.5 seconds, getting rid of duplicate and redundant tweets. A word cloud with classes: Positive, Negative, and Neutral was constructed which then used neural network to predict them, resulting in 97% training and 99% testing accuracy. Results aid in improved policy design, keeping citizens' perspectives in mind, and an aware government about issues like vaccination shortages, food, poverty, etc.

• Covid-19
• Pandemic
• Sentimental Analysis;
• Word Cloud,
• K-Means Clustering,
• Natural Language Processing,
• Twitter,
• Covaxin
• Covishield

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