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Abstract


A MAPREDUCE BASED DISTRIBUTED COMBINED SENTIMENT ANALYSIS MODEL AND APPLICATION

In this study, in order to eliminate the performance losses experienced in the processing of big data, a distributed combined model working on the Hadoop ecosystem was designed and developed. A new model was used by combining dictionary-based methods and machine learning-based methods which are commonly used in sentiment analysis. The combined model we developed has been programmed and implemented as both the distributed version on Hadoop architecture and the serial version on traditional programming architecture and performance results has been compared and reported. Parallel model on Hadoop Distributed File System, which we believe will contribute significantly to the literature, developed it in this study process, and used in big data analysis, has achieved a higher performance by significantly eliminating performance losses. In addition, with this study, it is aimed to keep a perspective on the migration-migrant-refugee-immigrant problem, which concerns many countries of the world. Twitter users in European countries were selected as the target audience. It has been determined that the perceptions of Twitter users included in the analysis vary by country. The results of the study showed that the reflex and reactions to the immigrant problem can vary from country to country. It is thought that these results also provide important data to the researchers.



Keywords
Big data, Hadoop, Migration, Multi nomial naive bayes, Sentiment analysis



Kaynakça Abbas, M., Memon, K. A., Jamali, A. A., Memon, S.,Ahmed, A., 2019. M

Gelişmiş Arama


Duyurular

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    Dergimizin Aralık sayısı 

    (31.12.2020)

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