Support vector machines for text categorization

Basu, Aritra; Walters, Clarence P.; Shepherd, Michael

doi:10.1109/hicss.2003.1174243

Cited by 100 publications

(44 citation statements)

References 4 publications

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“…[4] Figure 1: Example of SVM hyper-plane pattern [4] Naive Bayes Classification: A Naive Bayes classifier is a well-known and practical probabilistic classifier and has been employed in many applications. It assumes that all attributes (i.e., features) of the examples are independent of each other given the context of the class, i.e., an independence assumption.…”

Section: Support Vector Machinesmentioning

confidence: 99%

A Survey of Various Machine Learning Techniques for Text Classification

Chavan¹,

Manjare²,

Hegde³

et al. 2014

IJETT

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Sentiments are expressions of one's words in a sentence. Hence understanding the meaning of text in the sentence is of utmost importance to people of various fields like customer reviews in companies, movie reviews in movies, etc. It may involve huge text data to analyze and it becomes totally unviable for manually understanding the meaning of sentences. Classifier algorithms should be used to classify the various meaning of the sentences. By using pre-defined data to train our classifier and three different algorithms namely Naive Bayes, Support Vector Machines, Decision Trees, we can simplify the task of text classification. Using relevant results and examples we will prove that SVM is one of the better algorithms in providing higher accuracy over the other two algorithms i.e. Naive Bayes and Decision Tree.

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Section: Support Vector Machinesmentioning

confidence: 99%

A Survey of Various Machine Learning Techniques for Text Classification

Chavan¹,

Manjare²,

Hegde³

et al. 2014

IJETT

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show abstract

“…We chose to use Support Vector Machines (SVM) for our classifier (Basu et al, 2003;Burges, 1998;Cortes and Vapnik, 1995;Joachims, 1998;Vapnik, 1995). SVMs are commonly used to solve classification problems by finding hyperplanes that best classify data while providing the widest margin possible between classes.…”

Section: Classifiermentioning

confidence: 99%

Automatic identification of discourse moves in scientific article introductions

Pendar

Cotos

2008

Proceedings of the Third Workshop on Innovative Use of NLP for Building Educational Applications - EANL '08

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This paper reports on the first stage of building an educational tool for international graduate students to improve their academic writing skills. Taking a text-categorization approach, we experimented with several models to automatically classify sentences in research article introductions into one of three rhetorical moves. The paper begins by situating the project within the larger framework of intelligent computer-assisted language learning. It then presents the details of the study with very encouraging results. The paper then concludes by commenting on how the system may be improved and how the project is intended to be pursued and evaluated.

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“…Categorization of news text using SVM and ANN was carried out in [2]. In the overall comparison of SVM and ANN algorithms for the data set that was used, the results for both recall and precision over all conditions indicate significantly differences in the performance of the SVM algorithm over the ANN algorithm and since SVM is a less (computationally) complex algorithm than the ANN, they concluded that SVM is preferable at least for the type of data examined, i.e., many short text documents in a relatively few well populated categories.…”

Section: Related Workmentioning

confidence: 99%

A Text Classifier Model for Categorizing Feed Contents Consumed by a Web Aggregator

Longe¹,

Salami²

2014

ijacsa

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Abstract-This paper presents a method of using a Text Classifier to automatically categorize the content of web feeds consumed by a web aggregator. The pre-defined category of the feed to be consumed by the aggregator does not always match the content being consumed and categorizing the content using the pre-defined category of the feed curtails user experience as users would not see all the contents belonging to their category of interest. A web aggregator was developed and this was integrated with the SVM classifier to automatically categorize feed content being consumed. The experimental results showed that the text classifier performs well in categorizing the content of feed being consumed and it also affirmed the disparity in the pre-defined category of the source feed and appropriate category of the consumed content.

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Support vector machines for text categorization

Cited by 100 publications

References 4 publications

A Survey of Various Machine Learning Techniques for Text Classification

A Survey of Various Machine Learning Techniques for Text Classification

Automatic identification of discourse moves in scientific article introductions

A Text Classifier Model for Categorizing Feed Contents Consumed by a Web Aggregator

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