Public opinions of light rail service in Los Angeles, an analysis using Twitter data

Understanding commuters’ perceptions, attitudes, and behavior is an important component of transportation planning and management. Collecting such information using traditional survey or interview methods is costly and burdensome, but mining attitudinal data from social networking media could potentially provide insights into the temporal alignment of public opinion with transportation system dynamics. We demonstrate this potential by examining facets of public posts on Twitter about light rail transit services in Los Angeles in terms of sentiment analysis, topic modeling, and the interaction between posters and retweeters. Results provide new insights into how transit users present themselves and their opinions, engage with government agencies, react to events/policies, and share information with others on social media. We also demonstrate an interactive online interface that transit service providers could use to display and monitor real-time feedback and sentiment along different lines in the area’s light rail system.ye