Chowdhury, Samira Alam and Khatun, Murshida and Mohammad, Abdul Rahman and Akter, Fahmida (2019) Use NLP to Analyze Public Sentiment After Breaches and Correlate with Brand Perception and Revenue Changes. American Journal of Economics and Business Management, 2 (2). ISSN 2576-5973

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Abstract

The increasing occurrence of cybersecurity incidents and online discussions has created a need for analysing public sentiment to understand changes in brand perception. This research is aimed at analyzing social media opinions and assessing the perception of reputation based on sentiments using NLP techniques. In the research, a Twitter based sentiment data set is used, which consists of classified opinions about the various entities, and text analysis methods are used to extract the positive, negative and neutral sentiment patterns. The study follows quantitative exploratory research design, in which data pre-processing, sentiment distribution analysis, entity level comparison and measurement of reputation score are used in R Studio. The results show that there are differences of public response to the various entities as well as differences in opinion about the positive or negative reputation trends. The study emphasizes the need for NLP supported analysis of unstructured information from social media to make sense and provide support for reputation monitoring, business decision making and understanding the public response following critical events.

Item Type:	Article
Uncontrolled Keywords:	Cybersecurity, Social Media, Sentiments, NLP Techniques, Twitter, Positive, Negative, Neutral, Reputation, Business Decision-Making
Subjects:	H Social Sciences
Depositing User:	admin eprints
Date Deposited:	19 Jul 2026 13:30
Last Modified:	19 Jul 2026 13:30
URI:	http://eprints.umsida.ac.id/id/eprint/16814

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