IJSTR

International Journal of Scientific & Technology Research

Home About Us Scope Editorial Board Blog/Latest News Contact Us
0.2
2019CiteScore
 
10th percentile
Powered by  Scopus
Scopus coverage:
Nov 2018 to May 2020

CALL FOR PAPERS
AUTHORS
DOWNLOADS
CONTACT

IJSTR >> Volume 9 - Issue 5, May 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



VLS Algorithm: A New Approach to Sentiment Analysis

[Full Text]

 

AUTHOR(S)

CH.RayalaVinod Kumar,D.Lalitha Bhaskari ,P.Srinivasa Rao

 

KEYWORDS

Sentiment analysis, Machine learning, Opinion mining, Classification, Twitter, Tweet, Hash tag.

 

ABSTRACT

In this current era, people can express their views, opinions, exchanging of data and sharing their thoughts about different topics, trends and issues on Social media. Social Media takes a major role to maintain the persons’ emotional feelings on their day to day life. Sentiment Analysis is a method to analyze the views and find the polarity of the views. Twitter is a crucial forum where people share their ideas, views and feelings multiple times. Sentiment Analysis from Twitter is a method of tweet analysis. Sentimental values can be derived from the user via tweets. The polarity measure of the data indicates whether the user’s sentiments are positive, negative and neutral values on an aspect. It focuses on the tweet and the hash tag for understanding the situation in each aspect. In this research paper, firstly we performed Analysis of sentiments to categorize highly unstructured Twitter information. Secondly, a comparision of the proposed algorithm called Various #tags Live tweets Sentiment analysis (VLS) with Naive Bayes and Convolution Neural Networks is performed. Section three of the research methodology discusses how the algorithm is operating. The findings are reported in the experiment section produced by the Naive Bayes, CNN and VLS Algorithms. After Comparison we proved that our proposed algorithm works efficiently.

 

REFERENCES

[1]. I. King, J. Li and K. T. Chan, “A Brief Survey of Computational Approaches in Social Computing”, in Proc. of Int. Joint Conf. on Neural Network, 2009, pp. 2699-2706.
[2]. S. R. Barahate and V. M. Shelake, “A Survey and Future Vision of Data mining in Educational Field”, in Proc. 2nd Int. Conf. on Advanced Computing & Communication Technology, 2012, pp. 96-100.
[3]. Bing Liu, N. Indurkhya and F. J. Damerau, Handbook of Natural Language Processing, Second Edition, 2010, pp. 1-3860-68.
[4]. X. Chen, M. Vorvoreanu and K. Madhavan, “Mining Social Media Data to Understand Students’ Learning Experiences”, IEEE Transaction, 2014, vol. 7, no. 3, pp. 246-259.
[5]. Weil, Kevin (VP of Product for Revenue and former Big Data engineer, Twitter Inc.), "Measuring Tweets." Twitter Official Blog, February 22, 2010. [Online]. Available: http://www.internetlivestats.com/twitter-statistics. [Accessed: 19-Oct-2015].
[6]. Krikorian, Raffi (VP, Platform Engineering, Twitter Inc.), "New Tweets per second record, and how!" Twitter Official Blog. August 16, 2013.[Online]. Available: https:// blog.twitter.com/ 2013/ new-tweets-per- second-record-and-how. [Accessed: 19-Oct-2015].
[7]. Twitter Engineering, "200 million Tweets per day." Twitter Official Blog. June 30, 2011. [Online]. Available: https://blog.twitter.com/2011/200-million-tweets-per-day. [Accessed: 19-Oct-2015].
[8]. “Three Cool and Inexpensive Tools to Track Twitter Hashtags”, June 11, 2013. [Online]. Available http://dannybrown.me/2013/06/11/three-cool-toolstwitterhashtags/ [Accessed: 19-Oct-2015].
[9]. "Twitter turns six." Twitter Official Blog. March 21, 2012. [Online]. Available: https://blog.twitter.com/2012/twitter-turns-six. [Accessed: 19-Oct-2015].
[10]. N. Kasture and P. Bhilare, “An Approach for Sentiment analysis on social networking sites”, Computing Communication Control and Automation (ICCUBEA), 2015, pp. 390-395.
[11]. S. Bhuta, A. Doshi, U. Doshi and M. Narvekar, “A review of techniques for sentiment analysis Of Twitter data”, Issues and Challenges in Intelligent Computing Techniques (ICICT), 2014, pp. 583-591.
[12]. M. S. Neethu and R. Rajasree, “Sentiment Analysis in Twitter using Machine Learning Techniques”, in 4th Int. Conf. of Computing, Communications and Networking Technologies (ICCCNT), 2013, pp. 1-5.
[13]. S. Bahrainian and A. Dangel, “Sentiment Analysis using Sentiment Features”, in Int. joint Conf. of Web Intelligence and Intelligent Agent Technologies, 2013, pp. 26-29.
[14]. G. Gautam and D. Yadav, “Sentiment analysis of twitter data using machine learning approaches and semantic analysis”, in 7th Int. Conf. on Contemporary Computing, 2014, pp. 437-442.
[15]. A. Celikyilmaz, D. Hakkani-Tur and JunlanFeng, “Probabilistic model-based sentiment analysis of twitter messages”, IEEE Spoken Language Technology Workshop (SLT), 2010, pp. 79-84.
[16]. V. Sehgal and C. Song, “SOPS: Stock Prediction Using Web Sentiment”, in 7th IEEE Int. Conf. on Data Mining Workshop, 2007, pp. 21-26.
[17]. N. Altrabsheh, M. Cocea and S. Fallahkhair, “Sentiment analysis: towards a tool for analysing real-time students feedback”, in 26th International Conference on Tools with Artificial Intelligence, 2014, pp. 420-423.
[18]. Z. WANG, V. J. Chuan TONG, X. XIN and H. C. CHIN, “Anomaly Detection through Enhanced Sentiment Analysis on Social Media Data”, in 6th International Conference on Cloud Computing Technology and Science, 2014, pp. 918-922.
[19]. Jansen,B.J.; Zhang,M.; Sobel,K.; and Chowdury,A. (2009), “Twitterpower: Tweets as electronic word of mouth”, Journal of the American Society for Information Science and Technology 60(11):2169–2188.
[20]. Pak, A., and Paroubek, P (2010), “Twitter as a corpus for sentiment analysis and opinion mining”. In Proc. of LREC.
[21]. Wilson, T. Wiebe, J.; and Hoffmann, (P. 2009),”Recognizing contextual polarity: An exploration of features for phrase-level sentiment analysis. Computational Li nguistics”, 35(3):399–433.
[22]. David Zimbra, M. Ghiassi and Sean Lee, “Brand-Related Twitter Sentiment Analysis using Feature Engineering and the Dynamic Architecture for Artificial Neural Networks”, IEEE 1530-1605, 2016.
[23]. MondherBouazizi and TomoakiOhtsuki, “Sentiment Analysis: from Binary to Multi-Class Classification”, IEEE ICC 2016 SAC Social Networking, ISBN 978-1-4799-6664-6.
[24]. “Geospatially and literally analysing tweets”, Journal of Advanced Research in Dynamical and Control Systems Volume 9, Issue Special Issue 14, 2017, Pages 1002-1009
[25]. B. Gokulakrishnan, P. Plavnathan, R. Thiruchittampalam, A. Perera and N. Prasath, “Opinion Mining and Sentiment Analysis on aTwitter Data Stream”, in Int. Conf. on Advances in ICT for Engineering Regions, 2012, pp. 182-188.
[26]. CH. Rayala Vinod Kumar, D. Lalitha Bhaskari and P. Srinivasa Rao ,”Comparison of Sentiment Analysis on Various Twitter #Tags Using Machine Learning and Deep Learning Techniques”, Journal of Advanced Research in Dynamical and Control, IISN:1943-023X Volume 11 , 04-Special Issue, Pages: 23-31.
[27]. Abebe Tesfahun, and D. Lalitha Bhaskari, “Adaptive Random Forest based Intrusion Detection System for SCADA Networks.” International journal of Critical infrastructure Protection, Elsevier
[28]. https://emojipedia.org/twitter/