Feature based opinion analysis on social media tweets with association rule mining and multi‐objective evolutionary algorithms

2021 ◽  
Author(s):  
Shobha Rani Meesala ◽  
Sumathy Subramanian
Keyword(s):  
Social Media ◽  
Evolutionary Algorithms ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Multi Objective ◽  
Opinion Analysis ◽  
Feature Based

Sentiment Evolution Analysis and Association Rule Mining for COVID-19 Tweets

2021 ◽  
Vol 2 (2) ◽  
pp. 3-21
Author(s):  
Yassine Drias ◽  
Habiba Drias
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Sentiment Analysis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Early Stage ◽  
Rule Mining ◽  
Virus Propagation ◽  
The Third ◽  
Evolution Analysis

This article presents a data mining study carried out on social media users in the context of COVID-19 and offers four main contributions. The first one consists in the construction of a COVID-19 dataset composed of tweets posted by users during the first stages of the virus propagation. The second contribution offers a sample of the interactions between users on topics related to the pandemic. The third contribution is a sentiment analysis, which explores the evolution of emotions throughout time, while the fourth one is an association rule mining task. The indicators determined by statistics and the results obtained from sentiment analysis and association rule mining are eloquent. For instance, signs of an upcoming worldwide economic crisis were clearly detected at an early stage in this study. Overall results are promising and can be exploited in the prediction of the aftermath of COVID-19 and similar crisis in the future.

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