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Python > Learn Python > SVM is applicable only for numeric data or can also...
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SVM is applicable only for numeric data or can also be for textual data?

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SVM can work with textual data also. However you have to train the SVM to classify the text data. It is a usual practice to create a document-term matrix from the text that you have, to make SVM work easily on the data.
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SVMs work with numerical data only - needs to be a vector input. But you can transform textual data into word vectors so that we can use SVM on textual data. Some of the methods are TFIDF, word2Vec, GloVe and FastText.
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I think it is applicable for numeric data since it is based on categorized prediction.
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SVM is an supervised learning algorithm or concept used for classification and regression analysis. The implementation can be done for any numeric data, text data, symbols or images. The logic is to find the implementation of hyper plane for separation and the support vectors.
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Any learning algorithm for that matter does not work Directly on text. It needs to be converted to a structured matrix before applying algorithms.The methods involve - 1. Extensive cleaning of the data - simple regular expressions to building complex CFGs 2. Converting the unstructured data to structured...
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Any learning algorithm for that matter does not work Directly on text. It needs to be converted to a structured matrix before applying algorithms.The methods involve - 1. Extensive cleaning of the data - simple regular expressions to building complex CFGs 2. Converting the unstructured data to structured data using Vector Space models - Vectorizers, LSA, W2V etc. read less
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Certified Professional Trainer @ Magnitia

It is especially used for Mathematical operations with geometry.
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First of all, not sure what you mean by textual data. Do you mean text classification or categorical variable. Anyway, SVM works well with both. I have used it for text classification also, and found it to consistently provide the very high rate of accurate classifiers. Using the Sequential Minimal Optimization...
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First of all, not sure what you mean by textual data. Do you mean text classification or categorical variable. Anyway, SVM works well with both. I have used it for text classification also, and found it to consistently provide the very high rate of accurate classifiers. Using the Sequential Minimal Optimization (SMO) methods, learning the SVM model is very fast. read less
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Text classification also assists as a way of remembering and differentiating the types of data, making. Similarly SVM applicable to data points with nonlinear decision surfaces by engaging a system identified.
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Yes, you can use textual data in SVM, but you need to turn the text content into numerical feature vectors e.g. using bag of words or occurrence of frequencies. http://scikit-learn.org/stable/tutorial/text_analytics/working_with_text_data.html
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Machine Learning Architect with 13 Yrs of Exp in IT industry, Computer Sci Engg, NIT, Allahabad

Python Rs. 8k it Indies project on Python as well.
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