Social Media Mining on Taipei's Mass Rapid Transit Station Services based on Visual-Semantic Deep Learning

Abstract

For public transport operators, passengers’ comments towards their experience are valuable for promoting more friendly transportation services. This paper demonstrates that passenger-generated online comments can be used to assess railway transportation station services. The natural language processing and social media mining techniques that include establishing an opinion classification model through visual semantic fusion deep learning methods are applied to assess Taipei’s Mass Rapid Transit (MRT) station services from the internet opinions. An opinion monitoring system includes: (1) opinion mining to build a social media comment dataset on the ontology of MRT stations.; (2) proposing intent-sentiment, image-text relationship, and content type categories to assist accessing of passengers’ quality of experience; (3) constructing a classification model to classify the nature of opinions (4) proposing visualization to provide an intuitive information display dashboard to help Taipei’s MRT operator sense the sentiment-intention trends of comments on each station and access the current service level as well as part of the quality management assessment is also proposed.

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“…These approaches inevitably produced high-dimensional interpretations of language processing, giving rise to the curse of dimensionality. Compared to traditional ML models, models based on neural networks achieved extraordinary success on many tasks involving natural language thanks to the use of word embeddings [12]. Word embeddings are lowdimensional with distributed feature representations suited for natural languages.…”