A comparison of automatic labelling approaches for sentiment analysis
Biswas, Sumana Young, Karen Griffith, Josephine
Biswas, Sumana
Young, Karen
Griffith, Josephine
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Repository DOI
Publication Date
2022-07-11
Type
Conference Paper
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Citation
Biswas, Sumana, Young, Karen, & Griffith, Josephine. (2022). A comparison of automatic labelling approaches for sentiment analysis. Paper presented at the 11th International Conference on Data Science, Technology and Applications, Lisbon, Portugal, 11-13 July.
Abstract
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 Afi (More)
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Publisher
SciTePress
Publisher DOI
10.5220/0011265900003269
Rights
CC BY-NC-ND 3.0 IE