Affiliation:
1. Sakarya University, Engineering Faculty, Industrial Engineering Department, Sakarya, Turkey
Abstract
As the usage of social media has increased, the size of shared data has instantly surged and this has been an important source of research for environmental issues as it has been with popular topics. Sentiment analysis has been used to determine people's sensitivity and behavior in environmental issues. However, the analysis of Turkish texts has not been investigated much in literature. In this article, sentiment analysis of Turkish tweets about global warming and climate change is determined by machine learning methods. In this regard, by using algorithms that are determined by supervised methods (linear classifiers and probabilistic classifiers) with trained thirty thousand randomly selected Turkish tweets, sentiment intensity (positive, negative, and neutral) has been detected and algorithm performance ratios have been compared. This study also provides benchmarking results for future sentiment analysis studies on Turkish texts.
Subject
General Mathematics,General Medicine,General Neuroscience,General Computer Science
Cited by
21 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Mental Health on Twitter in Turkey: Sentiment Analysis with Transformers;Decision Making in Healthcare Systems;2024
2. Sentiment Analysis for the Natural Environment: A Systematic Review;ACM Computing Surveys;2023-11-10
3. Semantic Feature Extraction-Based Twitter Sentiment Analysis Using Atom Search Optimizer and Ensemble Classifier;2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE);2023-11-02
4. Multinomial Naive Bayes Based Machine Learning Analysis of Twitter Sentiment;2023 2nd International Conference on Edge Computing and Applications (ICECAA);2023-07-19
5. Process Improvement Study in a Tire Factory;International Journal of Computational and Experimental Science and Engineering;2023-06-30