Publications Public Sentiment toward I-710 Corridor Project from Based

This project developed software based on machine learning techniques to summarize discussions regarding transportation in California on social media. This tool is intended to reveal factors important to transportation users that may not be evident to transit agencies. The software uses topic modeling to cluster public messages related to transportation on the Twitter social media platform. Sentiment analysis techniques were then utilized to assign a polarity (positive, negative, or neutral) to each message and then aggregated by topic. The software is thus able to summarize the sentiment towards each automatically identified topic. It was found that the quality of topic identification depends on the size of the dataset, the number of topics that has to be specified to the topic modeling algorithm, and the positive/negative thresholds for the sentiment analysis algorithm. Abstract: With social media like Twitter gaining popularity in our daily life, bulk of data are emerging online. These data implies the information closely relevant to the preferences, tastes, attitudes and beliefs of the general public. Such information is useful in making public strategies, budgets or plans. In order to provide Caltrans with the information on the 710 Corridor Project and the five alternatives from Twitter users, we investigate the tweets related to the 710 Project during the time period from 2009 to August 3rd, 2015, analyze the sentiment evolution over time, and examine the sentiment for the five alternatives of the 710 study in this paper. The results can be used as a reference for the stakeholders to make decisions on the 710 Project in the future. Abstract: Public transportation agencies can obtain large amounts of information regarding timeliness, efficiency, cleanliness, ridership and other performance measures through paper and online surveys. However, these metrics are based on the interests of these agencies and do not necessarily represent the concerns of the customers. Recently, social media have become a platform for people to show their satisfaction or discontent about particular services and products (e.g., Twitter feeds, Yelp reviews, Change.org petitions). The goal of this project is to develop a tool using machine learning techniques to discover factors affecting public transit rider satisfaction using social media data. This tool will is intended to reveal that instance, a sense pride are that are not measured by initial Abstract: We applied "machine learning" (analysis that automatically classifies data based on previously seen examples) to identify if public posts made to Instagram expressed positive or negative sentiment toward the California Department of Transportation and its projects. The study applied machine learning to photographs, captions and comments from the social network, and showed that including both image and text data increased the accuracy of identifying sentiment. Abstract: With many social media platforms like Twitter gaining popularity, extremely large amounts of user-generated content are becoming available online. These contents are closely related to the preferences, attitudes and beliefs of the general public on a particular topic. To provide Caltrans with opinions from Twitter users on the five alternatives of the 710 Corridor Project, we investigate relevant tweets from 2009 to August 3, 2015, analyze how public sentiment evolves over time, and examine the general sentiment for the five alternatives. The results can be used as a reference for the decision-makers to select the most acceptable alternative for the 710 Project in the future. Abstract: