论文标题
半监督的学习方法，用于预测地方政府选举的南非政治情绪
Semi-supervised learning approaches for predicting South African political sentiment for local government elections
论文作者
论文摘要
这项研究旨在通过分析地方政府选举期间在Twitter上分享的情绪来了解南非政治背景。强调分析的重点是理解围绕四个主要政党ANC，DA，EFF和Actiona的讨论。通过基于图的技术，使用了一种半监督的方法，用于将大量可访问的Twitter数据标记为将推文分类为负和积极情绪。通过潜在主题提取进一步分析了表达负面情绪的推文，以发现与每个政党相关的关注的隐藏主题。我们的研究结果表明，南非Twitter用户的普遍情绪对所有四个主要政党的负面情绪为负，而对当前执政党的使用者中的负面情绪最糟糕，与涉及有关腐败，无能和负担的担忧有关。
This study aims to understand the South African political context by analysing the sentiments shared on Twitter during the local government elections. An emphasis on the analysis was placed on understanding the discussions led around four predominant political parties ANC, DA, EFF and ActionSA. A semi-supervised approach by means of a graph-based technique to label the vast accessible Twitter data for the classification of tweets into negative and positive sentiment was used. The tweets expressing negative sentiment were further analysed through latent topic extraction to uncover hidden topics of concern associated with each of the political parties. Our findings demonstrated that the general sentiment across South African Twitter users is negative towards all four predominant parties with the worst negative sentiment among users projected towards the current ruling party, ANC, relating to concerns cantered around corruption, incompetence and loadshedding.
