Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 04200 Kosice, Slovakia.
Sensors (Basel). 2021 Dec 27;22(1):155. doi: 10.3390/s22010155.
The article focuses on solving an important problem of detecting suspicious reviewers in online discussions on social networks. We have concentrated on a special type of suspicious authors, on trolls. We have used methods of machine learning for generation of detection models to discriminate a troll reviewer from a common reviewer, but also methods of sentiment analysis to recognize the sentiment typical for troll's comments. The sentiment analysis can be provided also using machine learning or lexicon-based approach. We have used lexicon-based sentiment analysis for its better ability to detect a dictionary typical for troll authors. We have achieved Accuracy = 0.95 and F1 = 0.80 using sentiment analysis. The best results using machine learning methods were achieved by support vector machine, Accuracy = 0.986 and F1 = 0.988, using a dataset with the set of all selected attributes. We can conclude that detection model based on machine learning is more successful than lexicon-based sentiment analysis, but the difference in accuracy is not so large as in F1 measure.
本文专注于解决在社交网络的在线讨论中检测可疑评论者这一重要问题。我们专注于一种特殊类型的可疑作者，即喷子。我们使用机器学习方法生成检测模型，以区分喷子评论者和普通评论者，还使用情感分析方法识别喷子评论的典型情感。情感分析也可以使用机器学习或基于词典的方法来提供。我们使用基于词典的情感分析来提高检测喷子作者特有的词典的能力。我们通过情感分析实现了准确率=0.95 和 F1=0.80。使用机器学习方法获得的最佳结果是由支持向量机实现的，准确率=0.986，F1=0.988，使用了包含所有选定属性的数据集。我们可以得出结论，基于机器学习的检测模型比基于词典的情感分析更成功，但在准确性方面的差异不如在 F1 度量方面那么大。
