TY - GEN
T1 - Organizational Online Reputation Measurement Through Natural Language Processing and Sentiment Analysis Techniques
AU - Orrego, Christian
AU - Villa, Luisa Fernanda
AU - Sepúlveda-Cano, Lina Maria
AU - Giraldo M, Lillyana M.
N1 - Funding Information:
Supported by Universidad de Medellín, Colombia.
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - The set of perceptions held by various groups based on history and expectations constitutes the reputation of organizations. There are multiple correct measurements of reputation since no general definition of the concept has been reached. ORM (Online Reputation Monitoring-management) systems oversee this measurement and have a sentiment analysis component to perform this task. The literature presents different frameworks or methodologies for measurement developed by academia and industry. These proposals’ common objective is to measure online reputation based on the opinions expressed by individuals close to the organization. In the absence of an automatic ORM system, it is necessary to perform this task manually within a company by a person; this can generate operational errors, delay processes, and make scalability impossible to increase the number of items reviewed (news, comments). These drawbacks can be mitigated by automating the measurement of a client’s online reputation. This paper contains the development of three methodologies from the literature to explore online reputation measurement starting from Twitter and Google News information sources. The implementation results conclude that the POS-Tagger elimination methodology generates the best result compared to the coded methodologies.
AB - The set of perceptions held by various groups based on history and expectations constitutes the reputation of organizations. There are multiple correct measurements of reputation since no general definition of the concept has been reached. ORM (Online Reputation Monitoring-management) systems oversee this measurement and have a sentiment analysis component to perform this task. The literature presents different frameworks or methodologies for measurement developed by academia and industry. These proposals’ common objective is to measure online reputation based on the opinions expressed by individuals close to the organization. In the absence of an automatic ORM system, it is necessary to perform this task manually within a company by a person; this can generate operational errors, delay processes, and make scalability impossible to increase the number of items reviewed (news, comments). These drawbacks can be mitigated by automating the measurement of a client’s online reputation. This paper contains the development of three methodologies from the literature to explore online reputation measurement starting from Twitter and Google News information sources. The implementation results conclude that the POS-Tagger elimination methodology generates the best result compared to the coded methodologies.
KW - E-reputation
KW - Online reputation
KW - Reputation assessment
KW - Reputation management
KW - Reputation measurement
KW - Sentiment analysis
UR - http://www.scopus.com/inward/record.url?scp=85116802456&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-86702-7_6
DO - 10.1007/978-3-030-86702-7_6
M3 - Contribución a la conferencia
AN - SCOPUS:85116802456
SN - 9783030867010
T3 - Communications in Computer and Information Science
SP - 60
EP - 71
BT - Applied Computer Sciences in Engineering - 8th Workshop on Engineering Applications, WEA 2021, Proceedings
A2 - Figueroa-García, Juan Carlos
A2 - Díaz-Gutierrez, Yesid
A2 - Gaona-García, Elvis Eduardo
A2 - Orjuela-Cañón, Alvaro David
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th Workshop on Engineering Applications, WEA 2021
Y2 - 6 October 2021 through 8 October 2021
ER -