Actionable pattern discovery for Sentiment Analysis on Twitter Data in clustered environment

Actionable Patterns are desired knowledge to be mined from large datasets. Action Rules are vital data mining method for gaining actionable knowledge from the datasets. They recommend actions which users can undertake to their advantage, or to accomplish their goal. Meta actions are the sub-actions...

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Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2018-01, Vol.34 (5), p.2849-2863
Main Authors: Ranganathan, Jaishree, Irudayaraj, Allen S., Bagavathi, Arunkumar, Tzacheva, Angelina A.
Format: Article
Language:English
Subjects:
Algorithms
Computing time
Data analysis
Data mining
Data processing
Datasets
Sentiment analysis
Social networks
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Summary:Actionable Patterns are desired knowledge to be mined from large datasets. Action Rules are vital data mining method for gaining actionable knowledge from the datasets. They recommend actions which users can undertake to their advantage, or to accomplish their goal. Meta actions are the sub-actions to the Action Rules, which intends to change the attribute value of an object, under consideration, to attain the desirable value. The essence of this paper is to propose a new optimized and more promising system, in terms of speed and efficiency, for generating meta-actions by implementing Specific Action Rule discovery based on Grabbing strategy (SARGS) algorithm, and to apply that for Sentiment Analysis on Twitter data. We perform a comparative analysis of meta-actions generating algorithmic implementation in Apache Spark driven system, conventional Hadoop driven system and Single node machine using the Twitter social networking data and evaluate the results. We implement corpus based Sentimental Analysis of social networking data, and test the total time taken by the systems and their sub components for the data processing. Results show faster computational time for Spark system compared to Hadoop MapReduce and Single node machine for the meta-action generation methods.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-169472