The week's pick
Random Articles
Reseach Article
Review on Text Mining Algorithms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 134 - Number 8 |
| Year of Publication: 2016 |
| Authors: Shivani Sharma, Saurabh Kr. Srivastava |
10.5120/ijca2016907972
|
Shivani Sharma, Saurabh Kr. Srivastava . Review on Text Mining Algorithms. International Journal of Computer Applications. 134, 8 ( January 2016), 39-43. DOI=10.5120/ijca2016907972
Abstract
Nowadays twitter microblog has become very popular in the conversation practice and in spreading awareness about various issues among the people. People share their short messages / tweets among their private / public social network. These messages are valuable for the number of tasks to identify hidden knowledge patterns from the discussions. Many research have been conducted on text classification. Text classification uses terms as features which can be grouped to vote for belongingness of a class. Text classification can be carried on twitter data and various machine learning algorithms can be used for feature based performance evaluation. In this context we have reviewed few papers taken from various sources like IEEE Xplore, ACM, Elsevier etc.
References
- Abdulkareem Alsudais, Gondy Leroy, Anthony Corso, 2014,”We know where are you tweeting from:Assigning a Type of Place To Tweets Using Natural Language Processing”,IEEE International Congress on Big Data.
- Bo Pang, Lillian Lee, Shivakumar Vaithyanathan, 2002, ”Thumb up Sentiments Classification Using Machine Learning Techniques”, Proceedings of EMNLP.
- Evgeniy Gabrilovich , ShaulMarkovitch , 2004, “Text Categorization with Many Redundant Features: Using Aggressive Feature Selection to Make SVMs Competitive with C4.5”, ICML.
- VandanaKorde, C NamrataMahender, March 2012, “Text Classification And Classifiers: A Survey”, International Journal of Artificial Intelligence & Applications(IJAIA),Vol 3, No 2,.
- Kamal Nigam, Andrew,Kachites, Mccallum, Sebastian Thrun, Tom Mitchell,”Text Classification from Labeled and Unlabeled Documents Using EM”, Kluwer Academic Publishers, Boston. Manufactured in Netherlands.
- Thorsten Joachims,” Text CategorizationWith Support Vector Machines: Learning With Many Relevant Features”
- Xiuju Fu, Christina Liew, Harold Soh, Gary Lee, Terence Hung, Lee-Ching Ng, 2007,”Time Series Infectious Disease Data Analysis Using SVM AND Genetic Algorithm”,IEEE.
- Danah Boyd, Scott Golder, Gilad Lotan, 2010,” Tweet, Tweet, Retweet: Conversational Aspects of Retweetingon Twitter”,IEEE.
- DursunDelen, Christie Fuller, Charles McCann, Deepa Ray, 2007,” Analysis of healthcare coverage: A Data Mining Approach”,Expert systems with applications
- Fréderic Godin,Viktor Slavkovikj, Wesley De Neve,” UsingTopic Models for Twitter Hashtag Recommendation”, International World Wide Web Conference committee (IC3W2).
- Shuang Yang, Alek Kolcz, Andy Schlaikjer, Pankaj Gupta, “Large-Scale High-Precision Topic Modeling on Twitter”, in the proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data
- Ilkyu Ha, Hohwan Park, Chonggum Kim,” Analysis of Twitter Research Trends based on SLR”,Advanced Communication Technology(ICACT), 2014 16th International Conference
Index Terms
Keywords