Welcome to our project on sentiment analysis of tweets about Covid-19! In this project, we used the Twitter API to gather a dataset of tweets with the hashtag #Covid-19, and then applied machine learning techniques to analyze the sentiment of the tweets.
This project was completed by a team of three people, who divided the work equally among themselves. The goal of the project was to use natural language processing and machine learning to understand the sentiment of tweets about Covid-19.
The dataset for this project was gathered using the Twitter API. We collected a total of 1000 tweets with the hashtag #Covid-19.
We used a combination of supervised and unsupervised machine learning techniques to analyze the sentiment of the tweets. First, we preprocessed the data by removing stop words and performing stemming. Then, we used a supervised learning algorithm (such as logistic regression or support vector machine) to classify the tweets as positive, negative, or neutral. Finally, we used unsupervised techniques (such as k-means clustering) to group the tweets into different categories based on their sentiment.
The results of our analysis showed that the majority of tweets about Covid-19 were neutral, with a smaller percentage being either positive or negative. We also found that certain words and phrases were more commonly associated with either positive or negative tweets.
Overall, this project provides valuable insights into the sentiment of tweets about Covid-19. These insights could be useful for organizations or individuals looking to understand public opinion on the pandemic.