Earticle

초록

영어

Big Data is a term used to identify the datasets that due to their large size, is very difficult to manage with traditional techniques. This data may be in the order of magnitude of petabytes. It can be found easily on web, especially on social media in the form of customer blogs, reviews and comments. Generally it is unstructured data or semi-structured data. One can use this big data to generate values by calculating sentiment score. Map Reduce is one of the most popular algorithm in Hadoop environment to perform such task. The objective of present research is to automate the process of extracting sentiments expressed about specific features of a product. For this purpose three datasets generated by Amazon for different types of electronics product reviews has been used. The data sets used consists of reviews of the products Nikon Coolpix 4300 Camera, Nokia 6601 mobile and the Canon G3camera. Map Reduce algorithm on Hadoop environment that is considered faster, reliable and fault-tolerant for processing big amounts of data in-parallel on large clusters, has been used to extract sentiment score.

목차

Abstract
 1. Introduction
 2. Literature Review
 3. Unstructured Big Data
 4. Sentiment Classification
 5. Distributed Processing of Huge Datasets on Hadoop
 6. Extracting the Sentiment Score of Customer Review using Map Reduce
 7. Experimental Setups
 8. Execution Methodology and Findings
 9. Conclusion
 References

키워드

저자

• Syed Imtiyaz Hassan [ Department of Computer Science & Engineering, Hamdard University, New Delhi, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605