Abstract
In this paper it was emphasized that machine learning techniques can achieve high performance in classification and work effectively and scalably with large data sets. The dataset used in this study was obtained from www.kaggle.com. A total of 67529 comments collected from people working at Google, Amazon, Netflix, Facebook, Apple and Microsoft were evaluated. The N-gram model is an important representation scheme in text mining. N-gram models are the unigram model (N = 1), bigram (N = 2), and trigram (N = 3). Three different weighting schemes as TP, TF, and TF-IDF, and three different weighting schemes for traditional machine learning-based analysis as N-gram model (bigram, unigram and trigram) was used. Five supervised learning algorithm was used to train models: Naive Bayes, Support Vector Machines (SVM), Logistic Regression (LR), K-Nearest Neighbor (KNN) and Random Forest (RF).
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