Scalable sentiment classification for Big Data analysis using Naïve Bayes Classifier | IEEE Conference Publication | IEEE Xplore

Scalable sentiment classification for Big Data analysis using Naïve Bayes Classifier


Abstract:

A typical method to obtain valuable information is to extract the sentiment or opinion from a message. Machine learning technologies are widely used in sentiment classifi...Show More

Abstract:

A typical method to obtain valuable information is to extract the sentiment or opinion from a message. Machine learning technologies are widely used in sentiment classification because of their ability to “learn” from the training dataset to predict or support decision making with relatively high accuracy. However, when the dataset is large, some algorithms might not scale up well. In this paper, we aim to evaluate the scalability of Naïve Bayes classifier (NBC) in large datasets. Instead of using a standard library (e.g., Mahout), we implemented NBC to achieve fine-grain control of the analysis procedure. A Big Data analyzing system is also design for this study. The result is encouraging in that the accuracy of NBC is improved and approaches 82% when the dataset size increases. We have demonstrated that NBC is able to scale up to analyze the sentiment of millions movie reviews with increasing throughput.
Date of Conference: 06-09 October 2013
Date Added to IEEE Xplore: 23 December 2013
Electronic ISBN:978-1-4799-1293-3
Conference Location: Silicon Valley, CA, USA

References

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