Sentiment Analysis in Turkish Tweets Using Different Machine Learning Algorithms

Understanding emotions in any written text is considered as a hot topic for many researchers in the field of text mining, especially with the large contribution of users over the web 2.0 and with the growth of the different social media platforms. In this study we analysed emotions on Turkish text and studied the sentiment within each document using Sentiment Analysis techniques. Sentiment Analysis is the process of identifying and evaluating the emotional states contained in texts. This study aimed to investigate the effect and accuracy rate of sentiment analysis in Turkish texts. Sentiment analysis is an important field of research that helps to obtain important data in many areas such as marketing, social media analysis, and customer feedback. A comprehensive data set consisting of Turkish tweets from Kaggle was used and the emotional states of the texts were labelled. This data set consists of a variety of tweets with different topics and emotional tones. Using natural language processing techniques and machine learning algorithms, the data set was processed, and the model was trained. Within the scope of the study, different root extraction methods and a vector space model were used. In addition, machine learning algorithms such as Naive Bayes, Random Forest, Decision Tree, Gradient Boosting, Bernoulli Naive Bayes, Logistic Regression, K-Neighbours-Classifier, and Support Vector Classifier were applied to evaluate accuracy. This study aims to emphasize the importance of sentiment analysis in Turkish texts, to examine the impact of the methods used and to form a basis for future studies.