Abstract:

Nowadays, with the increased use of computers, a surge in data production has emerged, making data access more convenient. In this context, a substantial amount of textual data is generated on e-commerce sites, social media, and various electronic platforms. Analyzing and extracting meaningful insights from this amassed data proves valuable for numerous institutions, organizations, and individuals. Sentiment analysis is a commonly employed technique to derive sentiments from textual data, and contemporary sentiment analysis models often leverage the high performance offered by deep learning approaches. Prior to model training, several pre-processing steps are typically applied to the text data. In this study, three distinct deep learning approaches were proposed for sentiment analysis. These models were analyzed on two different datasets: winvoker and Beyazperde. Hyper-parameters and depth of models were optimized using the Bayesian optimization method to enhance the accuracy of model. Additionally, the impact of various pre-processing techniques on model performance were assessed. When non-preprocessed data is utilized, the models trained on the winvoker dataset achieve an accuracy of 94.16%, while those trained on the Beyazperde dataset reach 86.64%. With the application of pre-processing, these accuracies improve to 94.64% for the winvoker dataset and 89.08% for the Beyazperde dataset. Based on these findings, it was concluded that the effect of pre-processing decreased and the accuracy was higher for the winvoker data set with a higher number of samples.

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