Sakarya University, Engineering Faculty, Industrial Engineering Department, Sakarya, Turkey.
Comput Intell Neurosci. 2020 Sep 7;2020:1904172. doi: 10.1155/2020/1904172. eCollection 2020.
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.
随着社交媒体的使用增加，共享数据的规模瞬间飙升，这已经成为环境问题的重要研究来源，就像热门话题一样。情感分析已被用于确定人们在环境问题上的敏感性和行为。然而，在文献中，对土耳其文本的分析并没有被广泛研究。在本文中，通过机器学习方法确定了关于全球变暖与气候变化的土耳其推文的情感分析。在这方面，通过使用经过监督方法（线性分类器和概率分类器）确定的算法，并使用随机选择的三万条土耳其推文进行训练，检测了情感强度（积极、消极和中立），并比较了算法性能比。本研究还为未来关于土耳其文本的情感分析研究提供了基准测试结果。
