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Copyright of International Anatolian Journal of Social Sciences / Uluslararasi Anadolu Sosyal Bilimler Dergisi is the property of Uluslararasi Anadolu Sosyal Bilimler Dergisi and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

Türkiye has experienced the most destructive earthquake disaster in recent years. On February 6, 2023, two earthquakes of more than seven magnitudes occurred in Türkiye, first in Pazarcık and then in Elbistan districts of Kahramanmaraş province. These earthquakes caused the deaths of more than 50 thousand people and billions of dollars in damage. The earthquakes had serious effects on every segment of society, including children. This study, which aimed to examine the opinions of individuals about children in such a big earthquake, examined 3.450 tweets sent in February 2023. While the researchers used Maxqda to analyze tweets regarding hashtags, likes, retweets, and source devices, they used Python for sentiment analysis. The findings highlighted that the words AFAD (Disaster and Emergency Management Presidency), education, and psychology were frequently used as hashtags in tweets about children. In addition, the study observed that tweets with very few likes and retweets did not arouse much interest in people. The findings of the sentiment analysis indicated that the tweets were quite negatively oriented. The study's findings can guide future actions and plans concerning the earthquake and children. [ABSTRACT FROM AUTHOR]