We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Mining Consumption Intent from Social Data: A Survey

Published on September 2016 by Faizan Khan, Samarjeet Borah, Ashis Pradhan
International Conference on Computing and Communication
Foundation of Computer Science USA
ICCC2016 - Number 1
September 2016
Authors: Faizan Khan, Samarjeet Borah, Ashis Pradhan
bae2d3a9-7472-458c-a2e0-86a4532c5683

Faizan Khan, Samarjeet Borah, Ashis Pradhan . Mining Consumption Intent from Social Data: A Survey. International Conference on Computing and Communication. ICCC2016, 1 (September 2016), 14-20.

@article{
author = { Faizan Khan, Samarjeet Borah, Ashis Pradhan },
title = { Mining Consumption Intent from Social Data: A Survey },
journal = { International Conference on Computing and Communication },
issue_date = { September 2016 },
volume = { ICCC2016 },
number = { 1 },
month = { September },
year = { 2016 },
issn = 0975-8887,
pages = { 14-20 },
numpages = 7,
url = { /proceedings/iccc2016/number1/26153-cc54/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Computing and Communication
%A Faizan Khan
%A Samarjeet Borah
%A Ashis Pradhan
%T Mining Consumption Intent from Social Data: A Survey
%J International Conference on Computing and Communication
%@ 0975-8887
%V ICCC2016
%N 1
%P 14-20
%D 2016
%I International Journal of Computer Applications
Abstract

Social Media is a rich source of information about the desires and needs of users to buy a product or service. There lies a huge opportunity in mining the Intent of users which can be applicable to the field of marketing, ecommerce, recommender systems, etc. This survey focuses on analyzing the techniques that can be used to mine the Intent of users from social data. Notable works that have contributed towards determining the Intent of users from social data has been highlighted.

References
  1. Ding, Xiao, et al. "Mining User Consumption Intention from Social Media Using Domain Adaptive Convolutional Neural Network. " Twenty-Ninth AAAI Conference on Artificial Intelligence. 2015.
  2. Wang, Jinpeng, et al. "Mining User Intents in Twitter: A Semi-Supervised Approach to Inferring Intent Categories for Tweets. " Twenty-Ninth AAAI Conference on Artificial Intelligence. 2015.
  3. Fu, B. , and T. Liu. "Weakly-supervised consumption intent detection in microblogs. " Journal of Computational Information Systems 6. 9 (2013): 2423-2431.
  4. Hollerit, Bernd, Mark Kröll, and Markus Strohmaier. "Towards linking buyers and sellers: detecting commercial intent on twitter. " Proceedings of the 22nd international conference on World Wide Web companion. International World Wide Web Conferences Steering Committee, 2013.
  5. Gupta, Vineet, et al. "Identifying Purchase Intent from Social Posts. "ICWSM. 2014.
  6. Dai, Honghua Kathy, et al. "Detecting online commercial intention (OCI). "Proceedings of the 15th international conference on World Wide Web. ACM, 2006.
  7. Wang, Jinpeng, et al. "Mining New Business Opportunities: Identifying Trend related Products by Leveraging Commercial Intents from Microblogs. " EMNLP. 2013.
  8. Duan, Junwen, Xiao Ding, and Ting Liu. "Mining Intention-Related Products on Online Q&A Community. " Social Media Processing. Springer Berlin Heidelberg, 2014. 13-24.
  9. Kouloumpis, Efthymios, Theresa Wilson, and Johanna D. Moore. "Twitter sentiment analysis: The good the bad and the omg!. " Icwsm 11 (2011): 538-541.
  10. Pak, Alexander, and Patrick Paroubek. "Twitter as a Corpus for Sentiment Analysis and Opinion Mining. " LREc. Vol. 10. 2010.
  11. A. Khan, B. Baharudin, L. H. Lee, K. Khan, "A Review of Machine Learning Algorithms for TextDocuments Classification", Journal of Advances Information Technology, vol. 1, 2010.
  12. Social Media Statistics, URL: http://wearesocial. net/, access date: 15/01/2016.
  13. Smith, Marc, et al. "NodeXL: a free and open network overview, discovery and exploration add-in for Excel 2007/2010. " Social Media Research Foundation (2010).
  14. Tweepy, URL: http://www. tweepy. org/, access date: 15/02/2016.
  15. R Development Core Team (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL: http://www. R-project. org, access date: 15/02/2016.
  16. Bird, Steven. "NLTK: the natural language toolkit. " Proceedings of the COLING/ACL on Interactive presentation sessions. Association for Computational Linguistics, 2006.
  17. Manning, Christopher D. , Mihai Surdeanu, John Bauer, Jenny Finkel, Steven J. Bethard, and David McClosky. 2014. The Stanford CoreNLP Natural Language Processing Toolkit In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55-60.
  18. Baldridge, Jason. "The opennlp project. " URL: http://opennlp. apache. org/index. html,(accessed 2 February 2016) (2005).
  19. Holmes, Geoffrey, Andrew Donkin, and Ian H. Witten. "Weka: A machine learning workbench. " Intelligent Information Systems, 1994. Proceedings of the 1994 Second Australian and New Zealand Conference on. IEEE, 1994.
  20. RapidMiner, URL: https://rapidminer. com,access date: 18/02/2016.
Index Terms

Computer Science
Information Sciences

Keywords

Consumption Intent Intent Mining Feature Extraction Text Classification Machine Learning.