Review on Text Mining Algorithms
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This paper reviews few papers taken from various sources like IEEE Xplore, ACM, Elsevier on text classification carried on twitter data and various machine learning algorithms used for feature based performance evaluation.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.read more
Citations
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Journal ArticleDOI
Systematic Homonym Detection and Replacement Based on Contextual Word Embedding
TL;DR: A novel approach for the detection of homonyms based on contextual word embedding that allows a word to be understood based on the context in which it appears is proposed.
Book ChapterDOI
Improved Clustering Technique Using Metadata for Text Mining
S. Tejasree,Shaik Naseera +1 more
TL;DR: This investigation investigates the using the methods for content mining, content grouping, common dialect handling, machine figuring out how to distinguish security dangers by mining the applicable data from unstructured log messages.
Journal ArticleDOI
Application of Text Mining in Effective Document Analysis: Advantages, Challenges, Techniques and Tools
TL;DR: This survey paper provides information and brief idea on text mining, its advantages, applications and various text mining techniques that can be used for effective and efficient document analysis that in turn will provide information to build product roadmaps and make better decisions about their activities.
Journal ArticleDOI
An Innovative Research Framework on Intelligent Text Data Classification System Using Genetic Algorithm
Journal ArticleDOI
TeknoAssistant : a domain specific tech mining approach for technical problem-solving support
TL;DR: In this article , a domain-specific tech mining method for building a problem-solution conceptual network aimed at helping technicians from a particular field to find alternative tools and pathways to implement when confronted with a problem is presented.
References
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Book ChapterDOI
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
TL;DR: This paper explores the use of Support Vector Machines for learning text classifiers from examples and analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task.
Journal ArticleDOI
Text Classification from Labeled and Unlabeled Documents using EM
TL;DR: This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents, and presents two extensions to the algorithm that improve classification accuracy under these conditions.
Proceedings ArticleDOI
Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter
TL;DR: This paper examines the practice of retweeting as a way by which participants can be "in a conversation" and highlights how authorship, attribution, and communicative fidelity are negotiated in diverse ways.
Journal ArticleDOI
Text classification and classifiers: a survey
V Korde,C N Mahender +1 more
TL;DR: This paper has tried to give the introduction ofText classification, process of text classification as well as the overview of the classifiers and tried to compare the some existing classifier on basis of few criteria like time complexity, principal and performance.
Proceedings ArticleDOI
Text categorization with many redundant features: using aggressive feature selection to make SVMs competitive with C4.5
TL;DR: A novel measure is developed that captures feature redundancy, and is used to analyze a large collection of datasets and shows that for problems plagued with numerous redundant features the performance of C4.5 is significantly superior to that of SVM, while aggressive feature selection allows SVM to beat C 4.5 by a narrow margin.