An end-user sentiment assessment tool to predict autonomous vehicles (AVs) adoption and commercialization.
Our study complements previous research on social media platform user sentiment on AVs by (a) introducing a dynamic component by observing sentiment across time and critical news events, (b) correlating key user-discussed topics with shifts in user sentiment, and (c) predicting user sentiment based on news’ underlying topic assessment. We will leverage NewsCatcherAPI to extract filtered topic-specific news posts from the last 5 years to train a model using topic scores for sentiment prediction.
Our research is part of a project in a text mining class at McGill University.
- Python
- R
- Sentiment analysis using VADER and TextAnalyzer
- GPT-augmented topic modeling
- Regression inference (ridge)
- Deep learning models (RNN)
- Reddit API
- NewsCatcherAPI for news scraping to extract topic-specific media postings and releases for model training data.
A project by Zachariya Sow, Paul Prindiville-Porto, Gabriel Abbas, and Artiom Bakhrakh.
- Academic: [email protected]
- Personal: [email protected]
- Achal Shankar Gupta, & Sharma, S. (2022). Analysis of Public Perception of Autonomous Vehicles Based on Unlabelled Data from Twitter
- Ding, Y., Korolov, R., Al) Wallace, W., & Wang, X. (Cara). (2021). How are sentiments on autonomous vehicles influenced? An analysis using Twitter feeds. Transportation Research Part C: Emerging Technologies, 131, 103356.