A Study on the Techniques of Sentiment Analysis for Unstructured Data using Big Data Analytics

Authors(2) :-Renuka Devi D, Dr. Swetha Margaret T A

The real time unstructured data often refers to the information that doesn’t follow the conventional storage of information in a row-column database. Unlike structured data it does not fit into relational databases. It is responsible for the Variety, one of the four V’s of Big Data. Sources like satellite images, sensor readings, email messages, social media, web blogs, survey results, audio, videos etc., follow unstructured data. Organizations go beyond “basic” analytics and dive deeper into unstructured data to do things such as predictive analytics, temporal and geospatial visualization, sentiment, and much more. The objective of this paper is to confer model of sentiment analysis and its various techniques. Future research directions in this field are determined based on opportunities and several open issues in Big Data analytics.

Authors and Affiliations

Renuka Devi D
Department of Computer Science, Stella Maris College, Chennai, India
Dr. Swetha Margaret T A
Department of Computer Science, Stella Maris College, Chennai, India

Opinion mining, Sentiment analysis, Unstructured data, Big Data

  1. Pang B, Lee L (2008) Opinion mining and sentiment analysis. Found Trends 2(1-2):1–135
  2. Liu B (2012) Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers
  3. Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York, NY, USA. pp 168–177
  4. Pang B, Lee L (2004) A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42Nd Annual Meeting on Association for Computational Linguistics, ACL ’04. Association for Computational Linguistics, Stroudsburg, PA, USA
  5. Gann W-JK, Day J, Zhou S (2014) Twitter analytics for insider trading fraud detection system. In: Proceedings of the second ASE international conference on Big Data. ASE
  6. Analyzing Text with the Natural Language Toolkit by Steven Bird, Ewan Klein, and Edward Loper.
  7. Naive_Bayes and Sentiment classification. In Stanford University.
  8. Diana Maynard, Adam Funk. Automatic detection of political opinions in tweets. In:   Proceedings of the 8th international conference on the semantic web, ESWC’11; 2011. p. 88–99.
  9. Bing Liu. Sentiment Analysis and Opinion Mining, Morgan & Claypool Publishers, May 2014.
  10. Comparison of Classification Algorithms in Text Mining. International Journal of Pure and Applied Mathematics [Volume 116 No.22 2017, 425-433]

Publication Details

Published in : Volume 3 | Issue 3 | March-April 2018
Date of Publication : 2018-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 223-227
Manuscript Number : CSEIT1833111
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Renuka Devi D, Dr. Swetha Margaret T A, "A Study on the Techniques of Sentiment Analysis for Unstructured Data using Big Data Analytics", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.223-227, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT1833111

Article Preview

Follow Us

Contact Us