论文标题
自动标签方法进行情感分析的比较
A Comparison of Automatic Labelling Approaches for Sentiment Analysis
论文作者
论文摘要
为监督机器学习的任务标记大量社交媒体数据不仅耗时，而且很困难和昂贵。另一方面，监督机器学习模型的准确性与训练标签数据的质量密切相关，自动情感标签技术可以减少人类标签的时间和成本。我们已经比较了三种自动情感标签技术：TextBlob，Vader和Afinn，在没有任何人为援助的情况下将情感分配给推文。我们比较三种情况：一种将培训和测试数据集与现有地面真相标签进行比较；第二个实验使用自动标签作为培训和测试数据集；第三实验使用三种自动标记技术来标记训练数据集并使用地面真相标签进行测试。在两个Twitter数据集上评估了实验：Semeval-2013（DS-1）和Semeval-2016（DS-2）。结果表明，Afinn标签技术使用BilstM深度学习模型获得了80.17％（DS-1）和80.05％（DS-2）的最高精度。这些发现表明，自动文本标签可以带来巨大的好处，并提出了人类标签工作时间和成本的可行替代方案。
Labelling a large quantity of social media data for the task of supervised machine learning is not only time-consuming but also difficult and expensive. On the other hand, the accuracy of supervised machine learning models is strongly related to the quality of the labelled data on which they train, and automatic sentiment labelling techniques could reduce the time and cost of human labelling. We have compared three automatic sentiment labelling techniques: TextBlob, Vader, and Afinn to assign sentiments to tweets without any human assistance. We compare three scenarios: one uses training and testing datasets with existing ground truth labels; the second experiment uses automatic labels as training and testing datasets; and the third experiment uses three automatic labelling techniques to label the training dataset and uses the ground truth labels for testing. The experiments were evaluated on two Twitter datasets: SemEval-2013 (DS-1) and SemEval-2016 (DS-2). Results show that the Afinn labelling technique obtains the highest accuracy of 80.17% (DS-1) and 80.05% (DS-2) using a BiLSTM deep learning model. These findings imply that automatic text labelling could provide significant benefits, and suggest a feasible alternative to the time and cost of human labelling efforts.
