# Comparison of Machine Learning Models for Sentiment Analysis of Big Turkish Web-Based Data +
## 4 Results  
This section presents the experimental results of sentiment analysis on a sizeable textual dataset of cosmetic product reviews in Turkish. The analyses were performed using the Python 3.11. Jupyter Notebook software language. In the first stage of the study, the dataset detailed in Table 1 was created, and data preprocessing was performed. It is an important step in performing the analyses of the study and obtaining accurate performance results. The large dataset used in the study caused various spelling errors, emoticons, emojis, unrelated words, or conjunctions to be frequently included in the texts due to the structure of natural language and \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Predicted**} & \multicolumn{2}{c}{**Actual**} \\ \cline{2-3}  & **Positive Sentiment** & **Negative Sentiment** \\ \hline Positive Sentiment & True Positive (TP) & False Positive (FP) \\ Negative Sentiment & False Negative (FN) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Confusion matrix for sentiment classification. subjective textual data. For this reason, the data preprocessing stages performed in the study are as follows:  
Step 1. Cleaning: In the data preprocessing stage, first, special characters, emojis, emoticons, punctuation marks, and extra spaces that cause noise and inconsistency in the raw data were removed. Thus, expressions that would not make sense in the product reviews and could negatively affect the analysis results were removed. The dataset consisted of only letters and words. Step 2. Conversion Process: Since the product reviews contain uppercase and lowercase inconsistencies, the conversion process was applied, and all letters in the sentences were converted to lowercase. For example, although the words'satisfied', 'Satisfied', or 'SATISFIED' have a positive meaning, the system can perceive them as different words due to letter differences. For this reason, all sentences were standardized in a uniform form. Step 3. Removal of Stop-words: It is the process of removing words from the text that do not help to determine the sentiment of the text during the analysis. Stop words are words that do not carry any information and are language-specific functional words that vary according to the language of the text [60,61]. Stop words are words that are programmed to ignore entries both when indexing and when removing them [62]. They lack significant centrality as they return large amounts of redundant information and have no analytical value [23,63]. In this step of the data preprocessing phase, Turkish words frequently used in the comments but not important in terms of sentiment or meaning were removed. Stop-words that did not contribute to the analysis and would cause misleading results were identified as follows and removed from the analysis:  
['bir','ve','veya', 'de', 'da', 'falan', 'falan', 'flean', 'fhn', 'az olsa', 'azda olsa', 'lakin', 'nebze olsa', 'nebzede olsa', 'gercekten', 'oncelikle', 'gibi', 'gbi', 'cok', 'bir', 'bi', 'br', 'bi', 'vs', 'bence', 'hic', 'hic', 'hicbir', 'bu', 'ile', 'la', 'ama', 'fakat', 'cunku', 'eger','ve va', 'falanda']  
Step 4. Normalization: One of the most important steps in the data preprocessing phase of the study is spelling normalization, which corrects spelling mistakes. Product reviews contain spelling mistakes since they consist of texts produced subjectively by consumers who have not mastered specific grammar rules or are writing randomly. On the other hand, people's use of prolongations in order to express their sentiments in texts also causes spelling mistakes. In the current dataset, the encountered and possible misspellings were corrected and recorded in the system, and the texts were brought to the correct form. Another reason for the normalization process is the structural form of Turkish. The fact that Turkish is an agglutinative language makes it possible to negate positive words by combining them with different phrases, thus leading to misleading results during the analysis.