Social media produces huge amounts of data every minute, which is caused by its conventional implementation over the past
years. Modernization in the industry have facilitated new ways of communications between people and created many business prospects.
Big Data in social media need efficient and sophisticated processing technologies. Rationale of data mining analyses is to discover
valuable patterns and insights from Twitter data. In today’s world, Social Networking websites like twitter, Facebook, tumbler, etc plays
a very significant role. Twitter is a micro-blogging which provides tremendous amount of data which can be used for various applications
of sentimental analysis like predictions, reviews, elections, marketing etc. Also for a business to execute successfully it is supportive to
identify the opinion viewpoints or reviews of the consumers and make amendments in the tactics and services accordingly. Similarly for
the consumers it is very valuable to distinguish about the eminence of the products and services in advance. Sentimental Analysis also
called opinion mining is a process of extracting information from large amounts of data and classifies them into different classes called
sentiments. Opinion mining is a technique of refining the information and revising the emotions associated with a exacting review and
consequently discovering the polarity of the review. In this paper, a study on different perception and move towards of Twitter data
analysis performed in recent years by means of opinion mining is prepared by taking into consideration the words, retweets, hash tags
and emotions.
Published In:IJCSN Journal Volume 7, Issue 1
Date of Publication : February 2018
Pages : 27-35
Figures :04
Tables : 01
Ch Srinivasa Rao : is a Research Scholar in the
Department of Computer Science & Engineering
at Acharya Nagarnuna University, Guntur, A.P,
India. He is working as Associate Professor in
SVKP & Dr K S Raju A&Sc College,
Penugonda, A.P. He received Masters Degree in Computer
Applications from Andhra University and Computer Science
Engineering from Jawaharlal Nehru Technological University,
Kakinada, India. His research interests include Data Mining,
Big Data Analytics.
Dr.G Satyanarayana Prasad : is Professor in the
Department of Computer Science Engineering and
Dean, Training & Placements at RVR & JC
College of Engineering, Chowdavaram, Guntur,
India. He received M.S in Computer Science from A&M University, ALABAMA, USA and PhD from Andhra
University, Visakhapatnam, India. His research interests
include Image Processing, Data Mining, Big Data Analytics.
He guided two research scholars for award of their PhD. He
published books, several papers in International conferences
and journals.
Dr.Vedula Venkateswara Rao : is Professor in
the Department of Computer Science
Engineering at Srivasavi Engineering College,
tadepalligudem, India. He received Matsers Degree in
Computer Science Engineering from JawaharLalNehru
Technological University Kakinada, Masters Degree In
Information Technology from Punjabi University, Patiayala,
India and PhD from Gitam University. His research interests
include Cloud Computing and Distributed Systems, Data
Mining, Big Data Analytics and Image Processing. He
published several papers in International conferences and
journals.
Opinion miming is an up-and-coming field of data
mining to dig out the knowledge from enormous volume
of data that may perhaps be customer comments,
feedback and reviews on any product or topic etc. Study
has been performed to extract opinions in structure of
document, sentence and feature level sentiment analysis.
It is observed that nowadays opinion mining inclination
is touching to the sentimental reviews of twitter data,
comments used in Face book on pictures, videos or Face
book status. This paper describes a wide-ranging, up-todate
assessment on the research work done in various
characteristics of sentimental analysis. This paper
summarizes some of the most commonly used
applications and challenges in sentiment analysis. Now
business organizations and academics are putting
forward their efforts to find best system for sentiment
analysis.
[1] Malhar Anjaria and Ram Mohana Reddy Guddeti, “
Influence Factor based opinion mining of Twitter data
using supervised learning” sixth IEEE conference on
COMSNETS, 6-10 Jan 2014, Bangalore, India.
[2] Ana c E S Lima and Leandro N de Castro, “ Automatic
sentiment Analysis of Twitter messages”, fourth IEEE
conference on CASoN, 21-23 Nov 2012, Sao Carlos.
[3] Dimitrios Kotsakos, Panos Sakkos, ioannis Katakis,
Dimitrios Guanopulos, “#tag: Meme or Event?”
IEEE/ACM conference on ASONAM, 17-20 Aug 2014,
Beijing, China.
[4] Mahnaz Roshanaei and Shivakant Mishra, “An Analysis
of positivity and Negativity attributes of users on Twitter
“, IEEE/ACM conference on ASONAM, 17-20 Aug
2014, Beijing, China.
[5] Tuan Anh Hoang, William w Cohen, Ee-Peng Lim, Dovy
Pierce, David R Redlawsk, “Politics, Sharing and
emotion in Microblogs”, IEEE/ACM conference on
ASONAM, 2013, New York, USA.
[6] John P Dickerson, vadim Kagan, V S Subrahmanian,
“Using sentiment to detect Bots on Twitter: Are Humans
more opinionated than Bots? “, IEEE/ACM conference
on ASONAM, 17-20 Aug 2014, Beijing, China.
[7] Alexander Pak and Patrick Paroubek. "Twitter as a
corpus for sentiment analysis and opinion mining",
Proceedings of the Seventh International Conference on
Language Resources and Evaluation (LREC' 10), may
2010.
[8] https://en.wikipedia.org/wiki/Twitter
[9] http://www.thecloudavenue.com/2013/03/analyse-
[10] Tweets-using-flume-hadoopand.
htmlhttp://blog.cloudera.com/
blog/2012/09/analyzing-
[11] Twitter-data-with-hadoop/
[12] J. Garside, “Twitter puts trillions of tweets up for sale to
data miners,” The Guardian. [Online]. Available:
[13].http://www.theguardian.com/technology/2015/mar/18/twit
ter-puts-trillions-tweets-forsale-data-miners. [Accessed:
18-Feb-2016].
[14] “Using machine learning to predict gender.” [Online].
Available:
https://www.crowdflower.com/using-machine-learningto-
predict-gender. [Accessed: 20-Feb-2016].
[15] “The Twitter glossary,” Twitter Help Center. [Online].
Available:
https://support.twitter.com/articles/166337. [Accessed:
20-Feb-2016].
[16]M. A. Russell, “Mining the Social Web, Second Edition,”
Published by O’Reilly Media, Inc., 1005 Gravenstein
Highway North, Sebastopol, CA 95472 in 2014, ISBN:
978-1-449-36761-9.
[17] “Using hashtags on Twitter,” Twitter Help Center.
[Online].Available:
https://support.twitter.com/articles/49309. [Accessed: 23-
Feb-2016].
[18] B. Pang, L. Lee, and S. Vaithyanathan, “Thumbs up?:
sentiment classification using machine learning
techniques,” Proceedings of the ACL-02 conference on
Empirical methods in natural language processing,
vol.10, 2002, pp. 79- 86.
[19] P. Turney, “Thumbs up or thumbs down? Semantic
orientation applied to unsupervised classification of
reviews,” Proceedings of the Association for
Computational Linguistics (ACL), 2002, pp. 417–424.
[20] M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M.
Stede, "Lexicon-based methods for sentiment analysis,”.
