[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120439-en":3,"doc-seo-120439-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120439,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Sentiment Analysis in Turkish Tweets Using Different Machine Learning Algorithms - Research Article","Understanding emotions in written text is a central topic in text mining, driven by the scale of Web 2.0 and the expanding role of social media. This study analyzes sentiment in Turkish text by applying sentiment analysis techniques to a labeled dataset of Turkish tweets collected from Kaggle. The work processes the data with natural language processing and machine learning, including root extraction methods and a vector space model, then evaluates model accuracy. Algorithms such as Naive Bayes, Random Forest, Decision Tree, Gradient Boosting, Logistic Regression, K-Neighbors, and Support Vector Classifier are used to measure performance and compare effectiveness for future research directions.","RESEARCH ARTICLE  \nSentiment Analysis in Turkish Tweets Using Different Machine Learning Algorithms  \nHunaida Avvad a†  , Ecem Ereren a   \na Department of Management Information Systems , İzmir Bakırçay University, İzmir, Türkiye † [hunaida.awwad@bakircay.edu.tr](hunaida.awwad@bakircay.edu.tr), corresponding author  \nRECEIVED AUGUST 12 , 2024 ACCEPTED SEPTEMBER 19, 2024  \nCITATION Avvad, H. , & Ereren , E. (2024) . Sentiment analysis in Turkish tweets using different machine learning algorithms. Artificial Intelligence Theory and Applications, 4(2), 107-120.  \nAbstract  \nUnderstanding emotions in any written text is considered 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 in 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, examine the impact of the methods used, and form a basis for future studies.  \nKeywords: sentiment analysis, Turkish text, machine learning, Turkish tweet  \n1. Introduction  \nSocial media platforms and Web 2.0 allowed people to share their experience and to express their feedback about many products/services that they received, the huge size of the written text on the Web 2.0 is considered a hot research topic for many researchers who focus on text mining in order to analyse emotions in any written text is considered a hot topic for many researchers in the field of text mining. Social media tools such as Twitter and Facebook have an important role to play as big data sources in the process of extracting information from any text. The most important reason for this is that the textdata produced by these applications is increasing significantly day by day. Sentiment Analysis (SA) has emerged as a field in which natural language processing, machine learning, and linguistic methods are used to understand the emotional tone of texts and  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than AITA must be honoured. Abstracting with credit is permitted, and providing the material is not used for any commercial purposes and  \nis shared in its entire and unmodified form. Request permissions [from info@aitajournal.com](from info@aitajournal.com)  \nArtificial Intelligence Theory and Applications, ISSN: 2757-9778. ISBN: 978-605-69730-2-4 © 2024 İzmir Bakırçay University  \nidentify emotional trends or moods in the text. SA can be applied in many areas , such as social media analyti","cbCailGgwXVP3m3t","https://ap.wps.com/l/cbCailGgwXVP3m3t","pdf",490948,1,14,"English","en",105,"# Introduction\n# Methodology and Data\n## Data Source and Labeling\n## Text Processing (NLP) and Feature Representation\n# Machine Learning Models\n## Baseline and Comparative Algorithms\n# Evaluation and Results\n# Conclusion and Future Work","[{\"question\":\"What is the main goal of the study on Turkish tweets?\",\"answer\":\"The study aims to investigate the effect and accuracy rate of sentiment analysis in Turkish texts and highlight how different methods influence performance.\"},{\"question\":\"Which dataset and labeling approach are used?\",\"answer\":\"A comprehensive dataset of Turkish tweets from Kaggle is used, and the emotional states of the texts are labeled for training and evaluation.\"},{\"question\":\"How are machine learning algorithms evaluated in the research?\",\"answer\":\"After processing the tweets using NLP with root extraction and a vector space model, the study applies multiple machine learning algorithms and compares their accuracy.\"}]","Sentiment Analysis in Turkish Tweets Using Different Machine Learning Algorithms - 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