Twitter based sentimental analysis of Covid-19 observations

Area of Science:

• Social Sciences
• Computational Linguistics
• Public Health

Background:

• Social media platforms facilitate widespread public expression of opinions and emotions.
• Analyzing vast amounts of text data is crucial for understanding public sentiment.
• Sentiment analysis offers a method to gauge individual viewpoints by examining expressed emotions.

Purpose of the Study:

• To analyze public attitudes and sentiment regarding the Corona Pandemic on social media.
• To determine the prevailing level of public optimism during the ongoing pandemic.
• To classify social media opinions on COVID-19 as positive, negative, or nonpartisan.

Main Methods:

• Utilized sentiment analysis techniques to process social media text data.
• Employed polarity analysis to categorize opinions.
• Focused on keywords such as "COVID", "Corona virus", and "COVID-19" to identify relevant posts.

Main Results:

• The research successfully classified social media opinions related to the Corona Pandemic.
• Identified the sentiment polarity (positive, negative, nonpartisan) of public discourse.
• Provided insights into public optimism levels during the health crisis.

Conclusions:

• Sentiment analysis is effective for evaluating public opinion on major global events like the Corona Pandemic.
• The polarity technique accurately categorizes sentiment expressed on social media.
• Findings contribute to understanding public perception and emotional responses to COVID-19.