Extracting the sentiment score of customer review from unstructured big data using map reduce algorithm

被引：0

作者：

Hassan S.I. ^{[1
]}

机构：

[1] Department of Computer Science and Engineering, Hamdard University, New Delhi

来源：

International Journal of Database Theory and Application | 2016年 / 9卷 / 12期

关键词：

Big data; HDFS; Map reduce; Opinion mining; Unstructured data;

D O I：

10.14257/ijdta.2016.9.12.26

中图分类号：

学科分类号：

摘要：

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. © 2016 SERSC.

引用

页码：289 / 298

页数：9

共 16 条

[11]

Zhang Y., Chen S., Wang Q., Yu G., i2MapReduce: Incremental MapReduce for Mining Evolving Big Data, IEEE Transactions on Knowledge and Data Engineering, 27, 7, (2015)

[12]

Khan A., Baharudin B., Sentiment Classification by Sentence Level Semantic Orientation using SentiWordNet from Online Reviews and Blogs, Int. J Comp Sci. Emerging Tech, 2, 4, pp. 539-552, (2011)

[13]

Move Large Files Fast: Overcoming the challenge of transferring huge unstructured data sets, (2015)

[14]

Potts C., On the negativity of negation, Proc. of SALT, pp. 636-659, (2011)

[15]

Davidov D., Tsur O., Rappoport A., Enhanced sentiment learning using Twitter hashtags and smileys, International Conference on Computational Linguistics: Posters (COLING '10), pp. 241-249, (2010)

[16]

Yang C.C., Wong Y.C., Wei C.P., Classifying web review opinions for consumer product analysis, Proceedings of the 11th International Conference on Electronic Commerce (ICEC '09), pp. 57-63, (2009)

← 1 2 →