Karamouzas Dionysios, Mademlis Ioannis, Pitas Ioannis
Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Soc Netw Anal Min. 2022;12(1):91. doi: 10.1007/s13278-022-00922-8. Epub 2022 Jul 26.
The high popularity of Twitter renders it an excellent tool for political research, while opinion mining through semantic analysis of individual tweets has proven valuable. However, exploiting relevant scientific advances for collective analysis of Twitter messages in order to quantify general public opinion has not been explored. This paper presents such a novel, automated public opinion monitoring mechanism, consisting of a semantic descriptor that relies on Natural Language Processing algorithms. A four-dimensional descriptor is first extracted for each tweet independently, quantifying text polarity, offensiveness, bias and figurativeness. Subsequently, it is summarized across multiple tweets, according to a desired aggregation strategy and aggregation target. This can then be exploited in various ways, such as training machine learning models for forecasting day-by-day public opinion predictions. The proposed mechanism is applied to the 2016/2020 US Presidential Elections tweet datasets and the resulting succinct public opinion descriptions are explored as a case study.
推特的高度普及使其成为政治研究的绝佳工具，同时通过对单条推文进行语义分析来挖掘观点已被证明具有价值。然而，利用相关科学进展对推特信息进行集体分析以量化公众舆论尚未得到探索。本文提出了一种新颖的自动公众舆论监测机制，该机制由一个依赖自然语言处理算法的语义描述符组成。首先为每条推文独立提取一个四维描述符，对文本的极性、冒犯性、偏差和形象性进行量化。随后，根据所需的聚合策略和聚合目标，对多条推文进行汇总。然后可以通过多种方式利用这些信息，例如训练机器学习模型来预测每日公众舆论。所提出的机制应用于2016/2020年美国总统选举的推文数据集，并将由此产生的简洁公众舆论描述作为案例研究进行探讨。
