Sentiment Trend Analysis of SpaceX Tweets Using Time-Series Sentiment Classification with TextBlob Algorithm

Abstract

This study explores the dynamics of public sentiment toward SpaceX, focusing on how it fluctuates in response to key events, including successful missions and technical setbacks. Using sentiment analysis on a dataset of SpaceX-related tweets, this research captures the emotional reactions of the public, classifying them into positive, neutral, and negative categories. The analysis reveals distinct patterns: positive sentiment predominates during major achievements, such as rocket launches and new technological advancements, while negative sentiment spikes following failures or delays. The results demonstrate how public perception of SpaceX is intricately tied to the company’s performance, reflecting both excitement for its successes and frustration for its setbacks. By examining these sentiment trends, this research offers insights into how companies in the space exploration sector can manage their public relations efforts and strategically engage with audiences on social media platforms. The study employs the TextBlob sentiment analysis tool, which classifies tweet polarity and subjectivity, to categorize public sentiment in a straightforward yet effective manner. Through time-series visualizations, the study tracks how sentiment evolves over time, highlighting key fluctuations tied to SpaceX’s milestones. Additionally, the research integrates visualizations like word clouds and bar charts to identify frequent keywords associated with both positive and negative sentiments, providing a deeper understanding of public discourse surrounding the company. The study underscores the role of Twitter as a significant tool for shaping public perception, particularly in high-visibility industries like space exploration, where real-time feedback can influence both public opinion and corporate strategies. This research contributes to the broader field of sentiment analysis by focusing on the tech and space industries, where public sentiment plays a pivotal role in shaping business success and technological innovation. By examining SpaceX’s public image through sentiment trends, this study highlights the importance of real-time sentiment monitoring in shaping company strategies. Future studies could extend this analysis to include other companies in the space sector or incorporate more sophisticated machine learning models for deeper sentiment classification.