Abstract
Opinion data collection is one of the most important forms of data analysis to understand and gain more insight about the trending information related to any domain or technology. The need for opinion data mining on the Twitter data is the demand of the indeed titled historical big data era. Blockchain is the technology which was introduced for cryptocurrency and later claimed to be embraced in most of the technologies because of its efficiency in ensuring privacy, security, and data management. With the increase in the popularity of blockchain technology, the opinion data collection for the blockchain technology is becoming compulsive to identify its substance in the practical application of different sectors. The utmost intention of this research is twitter data analysis centred on the domains that are believed to be domains that apply blockchain technology and hence ascertain that they are active and trending domains. The data analysis is performed on the tweets downloaded using tweepy API. This research engages different data visualizations, and Domain Identification by extracting features from tweets. The trend analysis for the opinion mining is accomplished by considering the re-tweets of the considered tweets. The proposed analysis is carried out on tweets which are carefully streamed using filter words related to the domain which claim to be active applicants of the blockchain technology. We will focus on techniques for the extraction of the Twitter data related to blockchain, processing, segregation, pattern visualization and trend identification by considering natural language processing paradigm.
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Mareddy, S., Gupta, D. (2022). Analysis of Twitter Data for Identifying Trending Domains in Blockchain Technology. In: Smys, S., Bestak, R., Palanisamy, R., Kotuliak, I. (eds) Computer Networks and Inventive Communication Technologies . Lecture Notes on Data Engineering and Communications Technologies, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-16-3728-5_49
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DOI: https://doi.org/10.1007/978-981-16-3728-5_49
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