Detection of key texts from Tweets in port systems

被引：0

作者：

Guillaume, Charles ^{[1
]}

Nunez, Cecilia ^{[1
]}

Duran, Claudia ^{[1
]}

Carrasco, Raul ^{[2
]}

Fuentealba, Diego ^{[3
]}

机构：

[1] Univ Tecnol Metropolitana, Dept Ind, Santiago, Chile

[2] Univ Las Amer, Fac Ingn & Negocios, Santiago, Chile

[3] Univ Tecnol Metropolitana Santiago, Dept Informat & Computac, Santiago, Chile

来源：

2021 IEEE CHILEAN CONFERENCE ON ELECTRICAL, ELECTRONICS ENGINEERING, INFORMATION AND COMMUNICATION TECHNOLOGIES (IEEE CHILECON 2021) | 2021年

关键词：

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

D O I：

10.1109/CHILECON54041.2021.9703075

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：859 / 863

页数：5

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