Analyzing Self-Driving Cars on Twitter
This provides insights into public opinion about autonomous vehicles for researchers and policymakers, though it's incremental in applying existing methods to this domain.
This paper analyzed public perception of self-driving cars using Twitter data, finding that people are both optimistic about the technology's future but concerned about its safety and reliability. For sentiment classification, Twitter-specific features like hashtags and linguistic features like emphatic words were identified as top predictors.
This paper studies users' perception regarding a controversial product, namely self-driving (autonomous) cars. To find people's opinion regarding this new technology, we used an annotated Twitter dataset, and extracted the topics in positive and negative tweets using an unsupervised, probabilistic model known as topic modeling. We later used the topics, as well as linguist and Twitter specific features to classify the sentiment of the tweets. Regarding the opinions, the result of our analysis shows that people are optimistic and excited about the future technology, but at the same time they find it dangerous and not reliable. For the classification task, we found Twitter specific features, such as hashtags as well as linguistic features such as emphatic words among top attributes in classifying the sentiment of the tweets.