application. Thus, we perform a series of tests with our method using different classification algorithms in order to choose the best one for this task. In all these tests, we used all 10 methods of 10SENT. We tested three different and widely used algorithms in our approach: *Support Vector Machine* (SVM) [@ChangSVM], *Random Forest* (RF) [@randomforest] and *$k$-Nearest Neighbors* (KNN) [@knn]. Here we used the implementations of RF and KNN provided in scikit-learn[^4] and for SVM, we use LibSVM[^5] package. Specifically, we use a *radial basis function* (RBF) kernel with a grid search for the best parameters. Overall, Random Forests produced the best results in most datasets, being the final choice for our bootstrapping method.

0

  **DATASET**               [KNN]{}             [SVM]{}              [Random Forest]{}
  ------------------------- ------------------- -------------------- -------------------
  **english\_dailabor**     55.5($ \pm $ 1.7)   58.2($ \pm $ 1.6)    60.9($ \pm $ 1.7)
  **aisopos\_ntua**         51.7($ \pm $ 4.3)   59.0($ \pm $ 1.0)    59.7($ \pm $ 2.5)
  **sentistrength\_digg**   46.0($ \pm $ 1.6)   51.7($ \pm $ 2.8)    49.4($ \pm $ 1.0)
  **debate**                40.6($ \pm $ 1.5)   23.7($ \pm $ 19.8)   42.6($ \pm $ 1.7)
  **sentistrength\_rw**     37.3($ \pm $ 5.0)   17.3($ \pm $ 14.6)   34.6($ \pm $ 1.8)

  : Results of different classifier algorithms for learning step of 