Fig 1.
The workflow of the proposed framework (a methodical diagram representing the entire process from data collection to topic modeling and find out the sentiment dynamics and clusters).
Fig 2.
Graphical representation of Twitter-LDA model.
Table 1.
Topic word distribution for top-k trending topics in Twitter.
Table 2.
Top-k trending topics for different values of α.
Table 3.
Top-k = 3 trending topics in different Im (we consider seven time intervals where len = 3 and Δt = 1).
Fig 3.
Heatmap representing the percentage of each topic discussed among users at various time interval Im (displays the top eight discussed topics at seven different time-intervals).
Table 4.
Sample tweets with sentiment polarity by VADER.
Fig 4.
Overall sentiment dynamics on Twitter at different time intervals Im for topic news.
Fig 5.
Overall sentiment dynamics on Twitter at different time intervals Im for topic health.
Fig 6.
Overall sentiment dynamics on Twitter at different time intervals Im for topic COVID-19 test.
Table 5.
Top 20 active users with their overall sentiment.
Fig 7.
Heatmap of selected ten users sentiment dynamics at each time-interval Im (shows the sentiments of selected ten users at different seven time-interval).
Table 6.
Topic-wise sentimental clusters size at different Im.
Table 7.
Overall sentimental clusters size for top-k = 3 trending topics.
Fig 8.
Overall sentimental clusters at different time intervals Im.
Table 8.
Entropy of sentimental clusters at different Im.
Table 9.
Schematic cohesion of sentimental clusters at different Im.
Fig 9.
Architecture of VADER.