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Twitter sentiment analysis for COVID-19 associated mucormycosis.
Singh, Maneet; Dhillon, Hennaav Kaur; Ichhpujani, Parul; Iyengar, Sudarshan; Kaur, Rishemjit.
  • Singh M; Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, India.
  • Dhillon HK; Department of Ophthalmology, Government Medical College and Hospital, Chandigarh, India.
  • Ichhpujani P; Department of Ophthalmology, Government Medical College and Hospital, Chandigarh, India.
  • Iyengar S; Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, India.
  • Kaur R; Principal Scientist, CSIR-Central Scientific Instruments Organisation, Chandigarh; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, Uttar Pradesh, India.
Indian J Ophthalmol ; 70(5): 1773-1779, 2022 05.
Article in English | MEDLINE | ID: covidwho-1835135
ABSTRACT

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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Full text: Available Collection: International databases Database: MEDLINE Main subject: Social Media / COVID-19 / Mucormycosis Type of study: Diagnostic study / Observational study / Prognostic study Limits: Humans Language: English Journal: Indian J Ophthalmol Year: 2022 Document Type: Article Affiliation country: Ijo.IJO_324_22

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Social Media / COVID-19 / Mucormycosis Type of study: Diagnostic study / Observational study / Prognostic study Limits: Humans Language: English Journal: Indian J Ophthalmol Year: 2022 Document Type: Article Affiliation country: Ijo.IJO_324_22