Singh Maneet, Dhillon Hennaav Kaur, Ichhpujani Parul, Iyengar Sudarshan, Kaur Rishemjit
Department of Computer Science and Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, India.
Department of Ophthalmology, Government Medical College and Hospital, Chandigarh, India.
Indian J Ophthalmol. 2022 May;70(5):1773-1779. doi: 10.4103/ijo.IJO_324_22.
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.
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.
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.
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.
在印度第二波新冠疫情期间，新冠病毒相关毛霉病（CAM）是一个严重的公共卫生问题。我们计划通过对推特数据进行情感分析来剖析公众对CAM的看法。
在这项观察性研究中，推特平台提供的应用程序编程接口（API）被用于通过使用与毛霉病（俗称“黑真菌”）相关的关键词，提取2021年5月3日至8月29日的实时对话。使用Vader情感分析工具对推文进行基于词典的情感分析。为了确定用户在任何给定主题上的总体情感，使用了一种基于情感给用户标记为“k”的算法。
在2021年5月3日至8月29日期间共收集到401037条推文，在2021年5月17日至5月23日观察到推文的峰值频率为160000条。与负面情感推文相比，正面情感推文占比更大，且存在每周变化。对公用事业需求的时间分析表明，需求在初期较高，但随时间下降，这与资源的可获得性有关。
使用推特数据进行的情感分析表明，在当前的新冠疫情大流行期间，社交媒体平台正越来越受欢迎，用于表达个人情感。在我们的研究中，基于时间对推文的评估显示负面情感推文的频率随时间减少。基于情感极性的用户转发网络中的两极分化表明用户之间联系紧密，突出了这样一个事实，即此类问题将我们的社会联系在一起，而非将其割裂开来。
