Özet:

Due to the increase in internet access opportunities and the widespread use of smartphones in recent years, services called social media where people share their opinions on various issues are widely used. Sentiment Analysis studies, which means making meaningful inferences about people's emotions on different subjects by analyzing social media data, and which is basically a classification process, is one of the prominent fields of study in recent years. In this study, 6 classification methods such as Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Artificial Neural Network (ANN) were used by using libraries in Python programming language. Within the scope of Sentiment Analysis of the algorithm, performance comparison was made. As the dataset, open source, labeled user comments on the IMDB site were used. Bag of Words (BoW), TF-IDF, FastText and Word2Vec text representation methods were used to represent the data set that was cleaned using Natural Language Processing methods. During the training and testing of the data set, the k-fold cross validation method was used, with k=5. The results obtained for 6 different classification methods were calculated by calculating accuracy, precision, recall and f1 score, and a detailed comparison was made and the results were recorded. As the highest accuracy values, LR and SVM gave similar results at 86% in BOW, 87% in TF-IDF, 87% in word2Vec and 83% in FastText, respectively.

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