Smart Simon Bot with Public Sentiment Analysis for Novel Covid-19 Tweets Stratification

Abstract

In present modern era, the outbreak of COVID-19 pandemic has created informational crisis. The public sentiments collected from different reflexions (hashtags, comments, tweets, posts of twitter) are measured accordingly, ensuring different policy decisions and messaging are incorporated. The implementation demonstrates intuition in to the advancement of fear sentiment eventually as COVID-19 approaches maximum levels in the world, by making use of detailed textual analysis with the help of required text data visualization. In addition, technical outline of machine learning stratification approaches are provided in the frame of text analytics, and comparing their efficiency in stratifying coronavirus tweets of different lengths. Using Naïve Bayes method, 91% accuracy is achieved for short tweets and using logistic regression classification method, 74% accuracy is achieved for short tweets.

Deep Learning‐Based Methods for Sentiment Analysis on Nepali COVID‐19‐Related Tweets

“…Their results show that both Twitter and news media provide similar kinds of information related to sentiment classification. Last but not the least, Ramya et al [ 21 ] adapted logistic regression and Naive Bayes classifiers to analyze sentiments of COVID-19-related tweets into positive, negative, and neutral. Their result shows that logistic regression and Naive Bayes impart 91% and 74% accuracy, respectively.…”

Emerging technologies for the management of COVID19: A review

“… ✓ ✗ ✗ ✓ IoT and Robotics digital screening tool for covid. Mobile application for covid sentiment ✗ ✗ ✓ ✗ [51] ✗ ✓ [70] Highlighting security issues due to covid- 19 mobile applications. ✗ ✗ …”

“…As a result, there is a need to examine the psychology of the human mind in such a situation [49] .The public sentiment gleaned from numerous reflexions (hashtags, comments, tweets, and Twitter postings) is accurately measured, ensuring that various policy decisions and communications are taken into account [50] . The application exhibits premonition in the improvement of terror sentiment finally as panemicreaches its peak globally by utilising extensive textual analysis with the assistance of essential text data visualisation [51] .Text mining is defined as "the process of extracting useful information from unstructured textual data by identifying and exploring interesting patterns." Text mining is not only more useful than data mining, but also significantly more sophisticated, as it uses software that combines components of database systems, artificial intelligence, machine learning, and quantitative statistics to filter huge amounts of unstructured data.…”

Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data

“…The majority of studies evaluated various ML algorithms. Only two studies used a single classifier [77], [109]. Most frequently used were RF [95], probabilistic classifiers such as NB [77], or other supervised models such as SVC [90] or logistic regression (LR) [83], [109].…”

“…Only two studies used a single classifier [77], [109]. Most frequently used were RF [95], probabilistic classifiers such as NB [77], or other supervised models such as SVC [90] or logistic regression (LR) [83], [109]. Two studies employed an ensemble classifier approach (in this paper, we consider RFs as not being an ensemble classifier).…”