  Traditional methods of collecting user feedback through transit surveys are
often time-consuming, resource intensive, and costly. In this paper, we propose
a novel NLP-based framework that harnesses the vast, abundant, and inexpensive
data available on social media platforms like Twitter to understand users'
perceptions of various service issues. Twitter, being a microblogging platform,
hosts a wealth of real-time user-generated content that often includes valuable
feedback and opinions on various products, services, and experiences. The
proposed framework streamlines the process of gathering and analyzing user
feedback without the need for costly and time-consuming user feedback surveys
using two techniques. First, it utilizes few-shot learning for tweet
classification within predefined categories, allowing effective identification
of the issues described in tweets. It then employs a lexicon-based sentiment
analysis model to assess the intensity and polarity of the tweet sentiments,
distinguishing between positive, negative, and neutral tweets. The
effectiveness of the framework was validated on a subset of manually labeled
Twitter data and was applied to the NYC subway system as a case study. The
framework accurately classifies tweets into predefined categories related to
safety, reliability, and maintenance of the subway system and effectively
measured sentiment intensities within each category. The general findings were
corroborated through a comparison with an agency-run customer survey conducted
in the same year. The findings highlight the effectiveness of the proposed
framework in gauging user feedback through inexpensive social media data to
understand the pain points of the transit system and plan for targeted
improvements.
