An omicron variant tweeter sentiment analysis using NLP technique
Sangamesh Hosgurmath, Vishwanath Petli, V K Jalihal
Abstract
Twitter is a miniature writing for a blog site which gives phase to individuals to share as well as communicate their perspectives about point, activities, items plus other medicinal harms. Tweets can be arranged keen on assorted classes reliant on their significance through the tip looked. NLP for wellbeing linked exploration be at present utilize in combination of tweet keen on positive as well as negative classes reliant on their approach utilizing normal language handling strategy. This paper contain execution of NLP (Bag of words) for message alliance reliant on twitter omicron tweet informational catalog utilizing sentiment preparing information utilizing twitter statistics set as well as suggest a plan to further expand categorization. Utilization of Lemmatization alongside NLP can further expand accuracy of characterization of tweets, via bountiful encouragement, pessimism as well as impartiality score of vocabulary present in tweet. For genuine effecting of this structure python through NLP plus twitter informational compilation be used. In this paper we are concerning feelings exploration in twitter tweet for omicron datasets to arrange the survey of all consumers whether it is positive, negative or impartial.