Tweets About Self-Driving Cars: Deep Sentiment Analysis Using Long Short-Term Memory Network (LSTM)

Due to the extensive growth of social media usage, sentiment analysis using social media data such as Twitter is an important task. The current study presents an empirical investigation of consumer sentiment toward self-driving cars or autonomous vehicles (AVs) based on the acquired self-driving car-related tweets. Information retrieval in social media is a complex task that requires technical insights. We used a hierarchical attention-based long short-term memory network (LSTM), a popular deep learning tool, to classify sentiment-specific document representations. The findings show that favorable attitudes toward AVs are associated with technological advantages and safety improvements, while more negative attitudes are associated with self-driving car-related crashes, media coverage, and deployment uncertainty. The results show that the estimated accuracy of LSTM is 85%. Our study indicates the necessity of examining big social media data in understanding the perceptions of end-users toward autonomous vehicles.