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.

本研究比较了三种情感自动标注技术用于推特情感分析，结果表明 Afinn 自动标注技术应用于 BiLSTM 深度学习模型具有最高的准确率，为 80.17％（DS-1）和 80.05％（DS-2），这表明自动文本标注可以提供显着的好处并提供了替代耗时和费用的人工标注工作的可行方案。