# Handle Imbalance in dataset

I am performing machine learning using SVM on sentiment analysis. I have imbalance in my dataset with class 1 having 5 times samples than class 0. I read that there are few things that we can do: (1) I cannot perform the undersampling as I have limited dataset (2) I performed oversampling using SMOTE and Random over sampling from imblearn.over_sampling. I am getting accuracy of 0.5, AUC of 0.5 and f-score keeps changing everytime I run the code (On the trainig data only, on testing f-score is just 0.006). This is behavior of random classifier, I think. (3) Using the class weight 5 times for class 0 than class 1 still gives the same result.

Now, I used the same feature earlier and got f-score of 0.67 and AUC of 0.73 which was good for me. I have just increased vocabulary and I am getting this result.