Abstract
Recent advancements in the field of Information Technology (IT) have not only changed the way people consume news but also made it possible for researchers to analyze a plethora of news, especially when the headlines are changing every minute. Big data, Natural Language Processing (NLP) and Machine Learning (ML) techniques are becoming staple for researchers of every domain to discover patterns and themes in the vast amount of data. This research would utilize NPL and ML techniques to analyze cybersecurity-related newspaper articles of major newspapers (digital version) from Japan and the US. Japan and the US are close allies, and they are collaborating in the field of cybersecurity owing to its rising significance for nations. However, as the demography, culture, and political behavior are different in both countries, it would be fascinating and very critical to analyze how newspapers from both countries are dealing with cybersecurity issues.
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This research was supported by JSPS KAKENHI Grant Number JP16K00480.
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Ghasiya, P., Okamura, K. (2020). Comparative Analysis of Japan and the US Cybersecurity Related Newspaper Articles: A Content and Sentiment Analysis Approach. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds) Advanced Information Networking and Applications. AINA 2020. Advances in Intelligent Systems and Computing, vol 1151. Springer, Cham. https://doi.org/10.1007/978-3-030-44041-1_39
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