利用Twitter数据进行公交用户反馈的情感分析：一种NLP框架
Leveraging Twitter Data for Sentiment Analysis of Transit User Feedback: An NLP Framework
AI总结：
提出一种基于NLP的框架，利用少样本学习和基于词典的情感分析技术，从Twitter数据中高效获取公交用户反馈，准确分类问题并评估情感，为公交系统改进提供低成本方案。
摘要：
通过公交调查收集用户反馈的传统方法通常耗时、耗费资源且成本高昂。在本文中，我们提出了一种基于NLP的新框架，该框架利用Twitter等社交媒体平台上可用的大量、丰富且廉价的数据，来了解用户对各种服务问题的看法。Twitter作为一个微博平台，拥有大量实时用户生成的内容，这些内容通常包含对各种产品、服务和体验的宝贵反馈和意见。该框架使用两种技术简化了收集和分析用户反馈的过程，无需进行昂贵且耗时的用户反馈调查。首先，它利用few-shot learning（少样本学习）在预定义类别内进行推文分类，从而有效识别推文中描述的问题。然后，它采用基于词典的情感分析模型来评估推文情感的强度和极性，区分正面、负面和中性推文。该框架的有效性在一部分手动标记的Twitter数据上得到了验证，并作为案例研究应用于NYC subway系统。该框架将推文准确分类为与地铁系统安全、可靠性和维护相关的预定义类别，并有效测量了每个类别内的情感强度。通过与同年进行的机构运营的客户调查进行比较，证实了一般发现。研究结果突出了所提出的框架通过廉价的社交媒体数据来衡量用户反馈的有效性，以了解公交系统的痛点并计划有针对性的改进。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
