Comparison of various Supervised Machine Learning Algorithms in Sentiment Analysis of Tweets on Black Fungus

Preethi, S.; Saroja, G Ashvika

doi:10.1109/icccnt51525.2021.9580094

Cited by 1 publication

(1 citation statement)

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“…Preethi and Saroja also studied sentiment analysis for mucormycosis, but their study duration was from May 1 to June 15, 2021 while ours was for a longer duration, from May 3 to August 29, 2021. [ 22 ]…”

Section: Discussionmentioning

confidence: 99%

Twitter sentiment analysis for COVID-19 associated mucormycosis

Singh

Dhillon

Ichhpujani

et al. 2022

Indian Journal of Ophthalmology

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Purpose: COVID-19-associated mucormycosis (CAM) was a serious public health problem during the second wave of COVID-19 in India. We planned to analyze public perceptions by sentiment analysis of Twitter data regarding CAM. Methods: In this observational study, the application programming interface (API) provided by the Twitter platform was used for extracting real-time conversations by using keywords related to mucormycosis (colloquially known as “black fungus”), from May 3 to August 29, 2021. Lexicon-based sentiment analysis of the tweets was done using the Vader sentiment analysis tool. To identify the overall sentiment of a user on any given topic, an algorithm to label a user “k” based on their sentiments was used. Results: A total of 4,01,037 tweets were collected between May 3 and August 29, 2021, and the peak frequency of 1,60,000 tweets was observed from May 17 to May 23, 2021. Positive sentiment tweets constituted a larger share as compared to negative sentiment tweets, with weekly variations. A temporal analysis of the demand for utilities showed that the demand was high in the initial period but decreased with time, which was associated with the availability of resources. Conclusion: Sentiment analysis using Twitter data revealed that social media platforms are gaining popularity to express one’s emotions during the ongoing COVID-19 pandemic. In our study, time-based assessment of tweets showed a reduction over time in the frequency of negative sentiment tweets. The polarization in the retweet network of users, based on sentiment polarity, showed that the users were well connected, highlighting the fact that such issues bond our society rather than segregating it.

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Section: Discussionmentioning

confidence: 99%