An analysis on the insights of the anti-vaccine movement from social media posts using k-means clustering algorithm and VADER sentiment analyzer

This study analyzes the insights and sentiments of the anti-vaccine movements in the social media. The data, comprising of tweets and excerpts, are pre-processed to omit noise and irrelevant data. They are clustered using the k-means clustering algorithm. Each word belonging to a cluster is processed by VADER sentiment analyzer. Prevalent sentiments per cluster label the mood associated in the cluster. The results suggest insights about vaccines such as: side effects, post-shot injuries, ineffectiveness, damage from ingredients, unvaccinated elite, reinforcement of the right to not vaccinate, toxic ingredients, big pharmaceuticals' profit maximization, links to autism, and health issues after getting vaccine shots. To evaluate k-means results, the silhouette score is determined to indicate how far a point is to other nearby clusters. The resulting average silhouette score of all points is 0.013540022 which indicates that the points are close to the decision boundaries.