Social Media-Based Analysis of Bus Service Satisfaction

Abstract

AbstractPeople post their emotional expressions about specific events through social media platforms, such as praise and complaints about city bus travel services. In this paper, we propose a social media-based method to analyze bus service satisfaction. Firstly, we collect microblogs related to bus service from Sina Weibo, and pre-process the collected microblogs. Then the Bidirectional Encoder Representations from Transformers (BERT) pre-trained model is combined with Bi-directional Long Short-Term Memory (BiLSTM) model to form BERT- BiLSTM model, using the pre-processed data to train the BERT-BiLSTM model to realize the classification of text data into positive and negative sentiments, and finally using the method of this paper to analyze and verify the satisfaction of Guangzhou bus services system in 2010. Comparing the method of this paper with traditional machine learning and deep learning methods, the experiments show the effectiveness of the BERT-BiLSTM model. KeywordsSocial mediaBus serviceSentiment analysisBERT-BiLSTMBinary classification