Detection of key texts from Tweets in port systems

Guillaume C.; Nunez C.; Duran C.; Carrasco R.; Fuentealba D.

Keywords: Natural Language Processing; Seaports; Sentiment Analysis; Social Media; Twitter

Abstract

Social media data is a rich source of information to analyze and detect potential problems from people. This work extracts Twitter's data related to two complex logistics companies to identify words, which can affect the strategy of ports. These words should enhance the business decision-making process to reduce social risks. The literature review suggested that TF-IDF and Latent Dirichlet Allocation can analyze the case studies. The results show that social networks can be linked to sustainable business aspects such as port planning and operations, information management, city, pandemic, ocean, culture, and people's beliefs.

Más información

Título según SCOPUS: Detection of key texts from Tweets in port systems
Título de la Revista: 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021
Editorial: Institute of Electrical and Electronics Engineers Inc.
Fecha de publicación: 2021
Idioma: English
DOI:

10.1109/CHILECON54041.2021.9703075

Notas: SCOPUS