MapReduce Functions to Analyze Sentiment Information from Social Big Data

Opinion mining, which extracts meaningful opinion information from large amounts of social multimedia data, has recently arisen as a research area. In particular, opinion mining has been used to understand the true meaning and intent of social networking site users. It requires efficient techniques...

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Bibliographic Details
Published in:International journal of distributed sensor networks 2015-01, Vol.2015 (6), p.417502
Main Authors: Ha, Ilkyu, Back, Bonghyun, Ahn, Byoungchul
Format: Article
Language:English
Subjects:
Accuracy
Analysis
Big data
Computational linguistics
Computer engineering
Contrarian investing
Data dictionaries
Data entry
Data mining
Data processing
Digital media
Ecosystems
Investment advisors
Language
Language processing
Load balancing (computing)
Manuals
Methods
Morphology
Multimedia
Natural language interfaces
Natural language processing
Online social networks
Sensitivity analysis
Sentiment analysis
Social networks
Social research
Studies
Texts
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Summary:Opinion mining, which extracts meaningful opinion information from large amounts of social multimedia data, has recently arisen as a research area. In particular, opinion mining has been used to understand the true meaning and intent of social networking site users. It requires efficient techniques to collect a large amount of social multimedia data and extract meaningful information from them. Therefore, in this paper, we propose a method to extract sentiment information from various types of unstructured social media text data from social networks by using a parallel Hadoop Distributed File System (HDFS) to save social multimedia data and using MapReduce functions for sentiment analysis. The proposed method has stably performed data gathering and data loading and maintained stable load balancing of memory and CPU resources during data processing by the HDFS system. The proposed MapReduce functions have effectively performed sentiment analysis in the experiments. Finally, the sentiment analysis results of the proposed system are very close to those of manual processes.
ISSN:1550-1329
1550-1477
1550-1477
DOI:10.1155/2015/417502