Please, how can I implement SVM in java from scratch ?

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I am implementing a sentiment analysis project and I now need to implement the last stage (SVM) the classifier.

What I have tried:

I understand SVM in theoretical part but now I want to start implementing it and I don't know how I can go about that

Posted 3-Oct-20 12:15pm

ShaggyPo

Updated 3-Oct-20 22:19pm

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Richard MacCutchan 4-Oct-20 3:38am

You will need to provide far more detail about your problem if you want a sensible response.

ShaggyPo 4-Oct-20 3:42am

I am doing sentiment analysis and I have done it to feature extraction where I used TF-IDF to obtain weights and now I need to implement SVM classifier

Richard MacCutchan 4-Oct-20 3:57am

Sorry but that does not explain anything. If you have a problem in your code then please provide the details.

ShaggyPo 4-Oct-20 4:04am

Thank you Richard for showing support. I don't mind showing you and explaining what I have done. To explain it more in details I think it is better we call on google meet up or hangout here (shaggyzee@gmail.com) because putting my code here and explaining is a lot

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Sandeep Mewara · Answer 1 · 2020-10-03T22:20:00

Look at the section 'Evaluation of the performance on the test set' at the following url: Working With Text Data — scikit-learn 0.23.2 documentation[^]

High level, example code would look like:

Python

feature_extraction = TfidfVectorizer()
X = feature_extraction.fit_transform(data["sentiments"].values)

X_train = X[:num_training]
X_test = X[num_training:]
y_train = data["Label"].values[:num_training]
y_test = data["Label"].values[num_training:]

# train classifier
clf = SVC(probability=True, kernel='rbf')
clf.fit(X_train, y_train)

# predict and evaluate predictions
predictions = clf.predict_proba(X_test)

Now, in case you want to re-invent wheel and have your own implementation of SVM, would suggest you star from here: scikit-learn/_classes.py at master · scikit-learn/scikit-learn · GitHub[^]