Search

Today, there are many channels where consumers can share their ideas about products and services. These opinions are usually in text format due to the nature of the feedback. Sentiment analysis is a topic that has gained importance in recent years, especially in text-based information sources. Aspect-based Sentiment Analysis, which is a more sensitive sentiment analysis technique, is the task of determining the aspect term, aspect category and sentiment class in a sentence. A data set consisting of the comments of restaurant customers presented to the competitors in the Semeval ABSA competition is used in the study. Using Word2vec, Glove, Fastext and Bert methods, the aspect term, aspect category and sentiment class are determined on the data set. The hypothesis is tested whether combining the word vector and the sentence vector can improve classification success for ABSA. In the classification made with four different vector methods, Fasttext method with 0.78 F1 score for the target term, Fasttext with 0.57 F1 score for the target category, and Bert method with 0.76 F1 score for the sentiment class have the most successful results. These results are compared with studies in the literature for different data sets and different languages. As a result, it is determined that Fasttext and Bert representation methods give successful results in sentiment analysis of targetbased Turkish language texts. [ABSTRACT FROM AUTHOR]