adding more data to Support Vector Classifier training

I am using the LinearSVC() available on scikit learn to classify texts into a max of 7 seven labels. So, it is a multilabel classification problem. I am training on a small amount of data and testing it. Now, I want to add more data (retrieved from a pool based on a criteria) to the fitted model and evaluate on the same test set. How can this be done?

Question:

It is necessary to merge the previous data set with the new data set, get everything preprocessed and then retrain to see if the performance improve with the old + new data?

My code so far is below:

def preprocess(data, x, y): 
  global Xfeatures 
  global y_train
  global labels
  porter = PorterStemmer()
  multilabel=MultiLabelBinarizer()
  y_train=multilabel.fit_transform(data[y])
  print("\nLabels are now binarized\n")
  data[multilabel.classes_] = y_train
  labels = multilabel.classes_
  print(labels)
  data[x].apply(lambda x:nt.TextFrame(x).noise_scan())
  print("\English stop words were extracted\n")
  data[x].apply(lambda x:nt.TextExtractor(x).extract_stopwords())
  corpus = data[x].apply(nfx.remove_stopwords)
  corpus = data[x].apply(lambda x: porter.stem(x))
  tfidf = TfidfVectorizer()
  Xfeatures = tfidf.fit_transform(corpus).toarray()
  print('\nThe text is now vectorized\n')
  return Xfeatures, y_train


 Xfeatures, y_train = preprocess(df1, 'corpus', 'zero_level_name')

Xfeatures_train=Xfeatures[:300]
y_train_features = y_train[:300]
X_test=Xfeatures[300:400]
y_test=y_train[300:400]
X_pool=Xfeatures[400:]
y_pool=y_train[400:]

def model(modelo, tipo):
  svc= modelo
  clf = tipo(svc)
  clf.fit(Xfeatures_train,y_train_features)
  clf_predictions = clf.predict(X_test)
  return clf_predictions 

preds_pool = model(LinearSVC(class_weight='balanced'), OneVsRestClassifier)