Feedback analysis using big data tools

With the ever increasing man-machine interaction, automation of process and decline in hardware and software cost, the amount of digital data generated and used is increasing day by day. The big data referred here is the massive amount of digital data generated in each and every second in structured, semi-structured and unstructured format throughout the world. This emerging field of big data analytic has driven the researcher worldwide toward design, development and implementation of various tools, technologies, architecture and platforms for analyzing the huge volume of data generated day to day. Big data consist of data sets which is difficult for legacy database management system to analysis. This paper details some analysis like feedback analysis, sentiment analysis and word-count. Feedback are important for the system enhancement, finding loop holes and as well as for proper work distribution. Feedback is valuable information that will be used to make good decision. Feedback is important not only when it highlights weaknesses but also for strengths. If analysis of feedback is done in wrong way then the result of analysis will also be wrong. As a result, the pattern identified will also be incorrect thus making the whole system incorrect as a whole. We will be implementing this proposed system for feedback analysis using Map-Reduce framework for processing large data set and for storage we will use Hadoop.

[1]  Pratik K. Agrawal,et al.  Textual Feedback Analysis: Review , 2015, 2015 International Conference on Computing Communication Control and Automation.

[2]  Swaminathan Natarajan,et al.  Sentiment analysis of Facebook data using Hadoop based open source technologies , 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

[3]  Hairong Kuang,et al.  The Hadoop Distributed File System , 2010, 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST).

[4]  Bing Liu.  Sentiment Analysis and Opinion Mining Opinion Mining , 2011 .

[5]  Srinivas Sethi,et al.  Architecture of efficient word processing using Hadoop MapReduce for big data applications , 2015, 2015 International Conference on Man and Machine Interfacing (MAMI).

[6]  Manjit Kaur,et al.  BIG Data and Methodology-A review , 2013 .

[7]  Ravi Narasimhan,et al.  Big Data - A Brief Study , 2014 .

[8]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[9]  Bing Liu,et al.  Sentiment Analysis and Opinion Mining , 2012, Synthesis Lectures on Human Language Technologies.

[10]  Ishwarappa,et al.  A Brief Introduction on Big Data 5Vs Characteristics and Hadoop Technology , 2015 .

[11]  Daniel E. O'Leary,et al.  Artificial Intelligence and Big Data , 2013, IEEE Intelligent Systems.

[12]  Kai Fan,et al.  An Adaptive Feedback Load Balancing Algorithm in HDFS , 2013, 2013 5th International Conference on Intelligent Networking and Collaborative Systems.

[13]  Alok Kumar,et al.  Sentiment analysis and Feedback Evaluation , 2015, 2015 IEEE 3rd International Conference on MOOCs, Innovation and Technology in Education (MITE).

[14]  Teng-Sheng Moh,et al.  A framework for fast-feedback opinion mining on Twitter data streams , 2015, 2015 International Conference on Collaboration Technologies and Systems (CTS).