# Naive Bayes / SVM classifiation - min. number of records (Python)

I am doing text classification with Python. I have around 120 records with 2 columns:

• text
• class

I tokenize, stem and lematize the words, I also did some of my own text preprocessing. When I run the alghoritms using sklearn and divide it to training and test sets each time I run the script the Accuracy Score is so different each time for both alghoritms. Sometimes I get around 70%, sometimes 40%. Is it because of the number of records (120) or not necessarily? if it is about number of records how much of them should I have?