Social Media Aided Sentiment Analysis in Forecasting

K.Nirmala Devi 1 *
More Detail
1 Kongu Engineering College, India
* Corresponding Author
Online Journal of Communication and Media Technologies, Volume 7, Issue 1, pp. 163-173. https://doi.org/10.29333/ojcmt/2585
OPEN ACCESS   2021 Views   1444 Downloads   Published online: 26 Jan 2017
Download Full Text (PDF)

ABSTRACT

User generated contents on web and social media grow rapidly in this emerging information age. Social media provides a platform for people to create contents, share them and bookmark them in a tremendous way. The exponential growth of social media arouses much attention on the use of public opinion to make better decisions about a particular product or person or service. The social media like online forums, Twitter, Facebook, blogs and microblogs are proving to be extremely valuable resources for anticipating, detecting and forecasting significant societal events. It provides a lot of opportunities for users to voice their opinions openly. The analysis of sentiments obtained through social media along with wisdom of crowds can automatically compute the collective intelligence of future performance in many areas like stock trend forecasting, box office sales, hot topic detection, election outcomes and so on. The proposed research aims to perform forecasting based on user sentiments in social media regarding hotspots and stock forecasting.
Keywords: social media, forecasting, sentiment analysis, opinion mining, wisdom of crowds

CITATION

Devi, K. (2017). Social Media Aided Sentiment Analysis in Forecasting. Online Journal of Communication and Media Technologies, 7(1), 163-173. https://doi.org/10.29333/ojcmt/2585

REFERENCES

  • Andrew. B ,Lawson, 2010, ‘Hotspot detection and clustering: ways and means’, Environ Ecol Stat, vol. 17, pp.231–245.
  • Bing Liu 2012, 'Sentiment Analysis and Opinion Mining', Morgan & Claypool Publishers.
  • Chen Wei &Chundi Parvathi 2011, 'Extracting hot spots of topics from time-stamped documents', Data & Knowledge Engineering,vol. 70, 2011, pp. 642–660.
  • Dang, Y, Zhang, Y & Chen, H 2010, 'A Lexicon-Enhanced Method for Sentiment Classification: An Experiment on Online Product Reviews', IEEE Intelligent Systems, vol. 25, no. 4, pp. 46–53.
  • Das, SR, & Chen, MY 2007, 'Yahoo! for Amazon: Sentiment extraction from small talk on the web', Management Science, vol.53, pp.1375–1388.
  • Feng, G, Guo, J, Jing, BY, &Hao, L 2012, 'A Bayesian feature selection paradigm for text classification', Information Processing & Management, vol.48, pp.283–302.
  • Gruhl, D, Guha, R, Kumar, R, Novak, J & Tomkins, A 2005, 'The predictive power of online chatter', Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pp. 78-87,New York.
  • Hong Liu &Xiaojun Li 2010,‘Internet Public Opinion Hotspot Detection ResearchBased on K-means Algorithm’, Proceedings of the ICSI , Part II, LNCS 6146, Springer-Verlag pp. 594–602.
  • JasminaSmailovic, Miha Grvcar1, Nada Lavrac& Martin Znidarsic 2013, 'Predictive Sentiment Analysis of Tweets: A Stock Market Application', HCI-KDD 2013, LNCS 7947, pp. 77–88, Springer-Verlag Berlin Heidelberg.
  • Kuan-Yu Chen, LuesakLuesukprasert, & Seng-cho T, Chou 2007, 'Hot Topic Extraction Based on Timeline Analysis and Multidimensional Sentence Modeling', IEEE Transactions on Knowledge and Data Engineering, vol.19, no. 8, pp.1016-1025.
  • Lee Zhong Zhen, Yun Huoy Choo, AzahKamilah Muda &Ajith Abraham 2013, ‘Forecasting FTSE Bursa Malaysia KLCI Trend with Hybrid Particle Swarm Optimization and Support Vector Machine Technique’, Proceedings of the World Congress on Nature and Biologically Inspired Computing, pp. 169-174.
  • Nirmala Devi, K &MuraliBhaskaran, V 2012, ‘Sentiment Analysis for Online Forums Hotspot Detection’, ICTACT Journal on Soft Computing, vol. 2, no. 2, pp.280-284.
  • Nizer, PSM &Nievola, JC 2012, 'Predicting published news effect in the Brazilian stock market', Expert Systems with Applications, vol.39, pp.10674–10680.
  • Ronen Feldman 2013, 'Techniques and Applications for Sentiment Analysis', Communications of the ACM, vol. 56, no. 4, pp. 82-89
  • Tumasjan, A, Sprenger, T, Sandner, P &Welpe, PI 2011, 'Election Forecasts With Twitter: How 140 Characters Reflect the Political Landscape', Social Science Computer Review, vol.29, no.4, pp. 402-418.
  • Xin Jin ,Andrew Gallagher, Liangliang Cao, Jiebo Luo & Jiawei Han 2010, 'The Wisdom of Social Multimedia: Using Flickr For Prediction and Forecast', Proceedings of the MM’10, Firenze, Italy.
  • Yanghui Rao , Qing Li , Xudong Mao & Liu Wenyin 2014, 'Sentiment topic models for social emotion mining', Information Sciences, vol. 266, pp. 90–100.