JacquelineArmijos2023/covid-vaccine-Sentiment-analysis-and-topic-modelling: Covid 19 vaccine Sentiment analysis and Topic modeling

After the covid-vaccine program has been implemented worldwide, vaccination is a hot topic in mass media, especially on Twitter. This study evaluated language attitudes of Covid-19 vaccine-related tweets from January 2020 to September 2022 through a Sentiment analysis and Topic modeling. A total number of 3525 tweets as cleaned or processed data was obtained. The data display only written texts about people's Covid-19 vaccine opinions, posted on their Twitter accounts; information such as geographical locations, dates, users' names were eliminated due to the nature of the study. According to the Sentiment analysis final results, the general sentiment polarity is neutral, and the number of neutral tweets represents approximately more than half of the total tweets, showing an absence of interest in commenting and reacting on the Covid-19 vaccine topic on Twitter. As an extra analysis, I selected 100 tweets from the Kaggle corpus and manually tagged them as positive, negative, or neutral. Then, the VADER dictionary was used to obtain compound scores for the tweets to reflect the sentiment. The studied sample, from a large and previous dataset, underwent a normality test, which included visualizations such as histograms and density curves. The analysis shows a relatively balanced distribution between positive and negative sentiment, with a significant proportion of neutral sentiment. Additionally, the null hypothesis was rejected. Instead, it indicated that the sentiment data did not follow a normal distribution. To robust the study, Topic modeling was used to categorize words into four groups, representing distinct topics related to the analysis. Therefore, Word clouds were generated to visualize the most salient words in each topic. They represented the sentiments and attitudes related to Covid-19 vaccines. Finally, this sample study provides insights into sentiment analysis, topic modeling, and data visualization techniques for studying tweets and user opinions on vaccines