Keyword Extraction Using Support Vector Machine

Finding domain of a research paper and a researcher is a crucial task and would be highly appreciable in order to provide personalized search results to the user. An automatic user domain classification technique based on SVM has been proposed in this paper in order to determine the domain of a user based on her publications. In this technique, for a given user, his specific area of domain is determined by classifying the keywords from his publication works. It consists of two phases: keyword extraction and domain classification. In keyword extraction phase, the list of publications corresponding to a user mail id is retrieved by using publish or perish tool. From each of the published papers, the keywords are extracted. In domain classification, SVM classifier is applied to determine the domain of the user. This is performed by training standard keywords from each domain into the SVM classifier. If a user belongs to more than one domain, then the primary domain with more publications will be considered.

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Conscientious Ant Colony Optimization Based Support Vector Machine for Text Document Classification

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.c8062.019320 ◽

2020 ◽

Vol 9 (3) ◽

pp. 1056-1060

Keyword(s):

Support Vector Machine ◽

Ant Colony Optimization ◽

Classification Accuracy ◽

Extraction Methods ◽

Document Classification ◽

Ant Colony ◽

Support Vector ◽

Keyword Extraction ◽

Efficient Manner ◽

Worst Case

Document classification indicates the keyword extraction and it become a thrust research in text mining research. The main purpose of keyword extraction is to classify the documents in a more efficient manner. Misclassification of documents may lead the results to worst case. Hence, there exists a need for optimization to precede the document classification more efficiently. In this paper Conscientious Ant Colony Optimization based Support Vector Machine is proposed to classify the documents. Different keyword extraction methods are available for extracting the contents from documents. Proposed classifier is ensemble with selected keyword extraction methods to increase the classification accuracy. Results shows that the proposed classifier has got better accuracy when ensemble with different keyword extraction methods. The results show that the proposed classifier has better performance in terms of Classification Accuracy and F-Measure, than baseline classifiers.

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Unsupervised two-stage keyword extraction from spoken documents by topic coherence and support vector machine

2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp.2012.6289053 ◽

2012 ◽

Cited By ~ 4

Author(s):

Yun-Nung Chen ◽

Yu Huang ◽

Hung-Yi Lee ◽

Lin-Shan Lee

Keyword(s):

Support Vector Machine ◽

Support Vector ◽

Keyword Extraction ◽

Two Stage ◽

Spoken Documents

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Support vector machine? – not only for MRI-images

10.1055/s-0039-3403050 ◽

2020 ◽

Author(s):

V Vasilevska ◽

K Schlaaf ◽

H Dobrowolny ◽

G Meyer-Lotz ◽

HG Bernstein ◽

...

Keyword(s):

Support Vector Machine ◽

Support Vector

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KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

Jurnal Pilar Nusa Mandiri ◽

10.33480/pilar.v15i2.693 ◽

2019 ◽

Vol 15 (2) ◽

pp. 275-280

Author(s):

Agus Setiyono ◽

Hilman F Pardede

Keyword(s):

Data Mining ◽

Support Vector Machine ◽

Decision Tree ◽

Naive Bayes ◽

Naïve Bayes ◽

Support Vector ◽

Spam Detection ◽

Support Vector Machine Algorithm ◽

Data Mining Techniques ◽

To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam. One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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