Survey on Classification Techniques for Data Mining
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TLDR
The different classification methods and classifiers that can be used for classification of observations that are initially uncategorized are compared to demonstrate the different accuracies and usefulness of classifiers.Abstract:
This paper focuses on the various techniques that can be implemented for classification of observations that are initially uncategorized. Our objective is to compare the different classification methods and classifiers that can be used for this purpose. In this paper, we study and demonstrate the different accuracies and usefulness of classifiers and the circumstances in which they should be implemented. General Terms Classification, Sentiment, Review, Accuracy, Positive, Negative, Neutral.read more
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References
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Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
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Support vector machines
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Oded Maimon,Lior Rokach +1 more
TL;DR: This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently.
Proceedings Article
Twitter as a Corpus for Sentiment Analysis and Opinion Mining
Alexander Pak,Patrick Paroubek +1 more
TL;DR: This paper shows how to automatically collect a corpus for sentiment analysis and opinion mining purposes and builds a sentiment classifier, that is able to determine positive, negative and neutral sentiments for a document.
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