Table 1.

Related work on topic modeling and sentiment analysis on long COVID–related data^a.

Authors	Source	Posters	Time	Location	Language	Topic modeling	Sentiment analysis
Déguilhem et al [47]	Twitter, Reddit, Doctissimo, Facebook, and other forums	Public	January 1, 2020, to August 10, 2021	France	French	Yes, biterm topic modeling [37]	No
Bhattacharyya et al [48]	Twitter	Public	August 28, 2022, to September 6, 2022	Not specified	English	No	Yes, National Research Council Emotion Lexicon [49]
Southwick et al [33]	Reddit	Public	Not specified	Not specified	English	Yes, LDAb [32]	Yes, Affective Norms for English Words lexicon [50]
Miyake and Martin [51]	Twitter, Facebook, blogs, news posts on social media, Reddit, forums, and other platforms	Public	January 1, 2020, to January 1, 2021	United Kingdom	English	No	Yes, but only of hashtags and emojis using IBM Watson emotional lexicon
Fu [34]	Twitter	Public	March 26, 2022, to April 26, 2022	United States	English	Yes, LDA [32]	Yes, VADERc [38]
Ramakrishnan et al [52]	Twitter	Public	May 1 to Sep 30, 2021	Not specified	English	No	Yes, IBM Watson Tone Analyzer and 6 classical MLd algorithms

^aThis table summarizes previous studies on topic modeling and sentiment analysis across various platforms and time frames. It includes information on the source of data, the population being studied, the time period covered, the geographic location, the language used, and the specific methodologies applied for topic modeling and sentiment analysis.

^bLDA: latent Dirichlet allocation.

^cVADER: Valence Aware Dictionary for Sentiment Reasoning.

^dML: Machine learning.