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Exploring an In-house Online Reputation Monitoring Implementation
Author(s) -
M S Mohd Ilias,
M A Najwa Hayaati,
A H Azni Haslizan
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1358/1/012056
Subject(s) - reputation , social media , sentiment analysis , computer science , outsourcing , analytics , business , data science , internet privacy , world wide web , marketing , social science , machine learning , sociology
Today, our online reputation can be developed through subjective opinions communication by netizen on social media. Online reputation especially for business entities can affect in many aspects such as sales and customer loyalty. Due to high amount of social data (e.g. comments), the manual approach in monitoring subjective opinions towards our brands, products or name is no longer relevant. Therefore, entities either organizations or individuals should monitor their online reputation using social media analytics tools such as sentiment analysis to mitigate reputation attack. However, Online Reputation Monitoring (ORMo) is yet a common practice where most practitioners are large corporations. Outsourcing is a good option but entities must allocate some costs which a burden for most small and medium entities. Thus, implementing social media analytics in-house ORMo by entities is a reasonable option. However, the guideline to implement in-house ORMo is still not well explored including what are the needed features in an ORMo tool. Therefore, this research attempts to explore on how to implement an in-house ORMo at affordable cost but reliable. In achieving the objective, this research involved four stages of investigations which are needs assessment of ORMo tool features, prototype development, simulation and expert survey for validation. This research found that in-house ORMo can be implemented at minimal cost using existing resources and the accuracy can be improved by updating the collection of words with its sentiment polarity. The results of this research can be the basis for an in-house ORMo tool implementation and for reviewing the existing ORMo tool.

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