TClustVID: A novel machine learning classification model to investigate topics and sentiment in COVID-19 tweets

Abstract

COVID-19, caused by SARS-CoV2 infection, varies greatly in its severity but presents with serious respiratory symptoms with vascular and other complications, particularly in older adults. The disease can be spread by both symptomatic and asymptomatic infected individuals. Uncertainty remains over key aspects of the virus infectiousness (particularly the newly emerging variants) and the disease has had severe economic impacts globally. For these reasons, COVID-19 is the subject of intense and widespread discussion on social media platforms including Facebook and Twitter. These public forums substantially influence public opinions and in some cases can exacerbate the widespread panic and misinformation spread during the crisis. Thus, this work aimed to design an intelligent clustering-based classification and topic extracting model named TClustVID that analyzes COVID-19-related public tweets to extract significant sentiments with high accuracy. We gathered COVID-19 Twitter datasets from the IEEE Dataport repository and employed a range of data preprocessing methods to clean the raw data, then applied tokenization and produced a word-to-index dictionary. Thereafter, different classifications were employed on these datasets which enabled the exploration of the performance of traditional classification and TClustVID. Our analysis found that TClustVID showed higher performance compared to traditional methodologies that are determined by clustering criteria. Finally, we extracted significant topics from the clusters, split them into positive, neutral and negative sentiments, and identified the most frequent topics using the proposed model. This approach is able to rapidly identify commonly prevailing aspects of public opinions and attitudes related to COVID-19 and infection prevention strategies spreading among different populations.

Keywords: COVID-19; Classification; Machine learning; TClustVID; Topics modeling; Twitter data.

Fig. 1

Details of working methodology where A. Data preprocessing B. Traditional classification and evaluation C. Clustering, classification and evaluation D. Comparison the outcomes between traditional and TClustVID E. Select the best clusters/datasets and Identify positive, neutral and negative clusters F. Extract topics by LDA and represent top frequent topics from it.

Fig. 2

Average performance of various classifiers for evaluating them using (a) traditional way (b) TClustVID corresponding to the nine twitter experimental datasets.

Fig. 3

Compute SHAP values to determine COVID-19 (a) Positive (b) Neutral (c) Negative topics.

Fig. 4

Word cloud of various topics.

Fig. 5

Positive topics of Cluster-3.

Fig. 6

Neutral topics of Cluster-3.

Fig. 7

Negative topics of Cluster-3.

Fig. 8

Top frequency of (a) Positive (b) Neutral (c) Negative COVID-19 associated topics.