Action Rules for Sentiment Analysis on Twitter Data using Spark

Action Rules are vital data mining method for gaining actionable knowledge from the datasets. 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 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. For this, we perform a comparative analysis of meta-actions generating algorithmic implementation in Apache Spark driven system and conventional Hadoop driven system using the Twitter social networking data and evaluate the results. We perform corpus based Sentimental Analysis of social networking data, and test the total time taken by both the systems and their sub components for the data processing. Results show faster computational time for Spark system compared to Hadoop MapReduce for implementing the meta-action generation methods.