  This study's goal is to create a model of sentiment analysis on a 2000 rows
IMDB movie comments and 3200 Twitter data by using machine learning and vector
space techniques; positive or negative preliminary information about the text
is to provide. In the study, a vector space was created in the KNIME Analytics
platform, and a classification study was performed on this vector space by
Decision Trees, Na\"ive Bayes and Support Vector Machines classification
algorithms. The conclusions obtained were compared in terms of each algorithms.
The classification results for IMDB movie comments are obtained as 94,00%,
73,20%, and 85,50% by Decision Tree, Naive Bayes and SVM algorithms. The
classification results for Twitter data set are presented as 82,76%, 75,44% and
72,50% by Decision Tree, Naive Bayes SVM algorithms as well. It is seen that
the best classification results presented in both data sets are which
calculated by SVM algorithm.
