语言无关的基于远监督的情感标注：以英语、塞佩迪语和塞斯瓦纳语为例
Language-Independent Sentiment Labelling with Distant Supervision: A Case Study for English, Sepedi and Setswana
AI总结：
本文提出一种语言无关的情感标注方法，利用emoji和词语信息，对英语、塞佩迪语和塞斯瓦纳语的推文进行情感标注，准确率分别达到66%、69%和63%。
摘要：
情感分析是一项有助于自动分析各种主题上意见和情绪的任务，应用于人工智能造福社会、人工智能在教育或营销等领域。虽然许多情感分析系统是为英语开发的，但许多非洲语言由于缺乏数字语言资源，如带有相应情感类别的标注文本，而被归类为低资源语言。其中一个原因是手动标注文本数据耗时且昂贵。因此，需要自动且快速的过程来尽可能减少手动工作量，使标注过程尽可能高效。在本文中，我们提出并分析了一种自动语言无关的情感标注方法，该方法利用带有情感的emoji和词语的信息。我们的实验使用了来自SAfriSenti多语言情感语料库的英语、塞佩迪语和塞斯瓦纳语的推文。我们显示，我们的情感标注方法能够对英语推文进行66%的准确率标注，对塞佩迪语推文进行69%的准确率标注，对塞斯瓦纳语推文进行63%的准确率标注，因此平均只有34%的自动生成标签需要修正。
英文摘要：
Sentiment analysis is a helpful task to automatically analyse opinions and emotions on various topics in areas such as AI for Social Good, AI in Education or marketing. While many of the sentiment analysis systems are developed for English, many African languages are classified as low-resource languages due to the lack of digital language resources like text labelled with corresponding sentiment classes. One reason for that is that manually labelling text data is time-consuming and expensive. Consequently, automatic and rapid processes are needed to reduce the manual effort as much as possible making the labelling process as efficient as possible. In this paper, we present and analyze an automatic language-independent sentiment labelling method that leverages information from sentiment-bearing emojis and words. Our experiments are conducted with tweets in the languages English, Sepedi and Setswana from SAfriSenti, a multilingual sentiment corpus for South African languages. We show that our sentiment labelling approach is able to label the English tweets with an accuracy of 66%, the Sepedi tweets with 69%, and the Setswana tweets with 63%, so that on average only 34% of the automatically generated labels remain to be corrected.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
