City Transit Rider Tweets
Understanding Sentiments and Politeness
Bibliographic Data
| ID | 15318901 |
|---|---|
| Authors | Subasish Da (0000-0002-1671-2753, Texas A&M Transportation Institute, corresponding author), Hamsa Zubaidi (0000-0003-1311-2098, Oregon State University), Hamsa Abbas Zubaidi (Oregon State University) |
| Year | 2021 |
| Volume | 30 |
| Issue | 1 |
| Pages | 111-126 |
| Publication date | 2021-04-26 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Urban Technology (JOURNAL) |
| Journal identifiers | ISSN: 1063-0732 • E-ISSN: 1466-1853 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/10630732.2021.1903288 |
| OpenAlex | W3198402639 |
| Language | EN |
| Citations received | 2 |
| References cited | 15 |
With the expanding popularity of Web 2.0, there has been a huge surge in the use of social media, like Twitter, to express user sentiments or opinions. Delays and breakdowns in transit operations can make riders annoyed and irritated, and as a result, they express their anger and frustration via social media posts. Understanding the tipping points of public frustration will help in developing better solutions. This study aims to develop a framework by developing multilevel sentiment analysis and determine the emotion and politeness measures using transit-related tweets from New York (New York City) and California (San Francisco). The popular hashtags associated with the transit systems of New York and California were collected during 2019. The words associated with negative sentiments widely differ in these two states. Moderate levels of differences are seen in the politeness measures for these two states. Additionally, co-occurrence measures associated with negative emotions identified unique issues based on the demographics. This study demonstrates that Twitter provides a great opportunity to understand the public perception of transit, and the findings can help authorities design a more efficient transit system to improve user experience
Advertising · Anger · Business · Internet privacy · Perception · Politeness · Political science · Politics · Popularity · Public opinion · Public relations · Public transport · Sentiment analysis · Social media · Sociology · World Wide Web · Computer Science · Digital Marketing and Social Media · Law · Psychology · Sentiment Analysis and Opinion Mining · Social Psychology · Transportation Planning and Optimization
| Citations per year | 0.67 |
|---|---|
| Citation span | 2023 - 2025 (3) |
| Highly cited | No |