Building and evaluating resources for sentiment analysis in the Greek language

Tsakalidis, Adam; Papadopoulos, Symeon; Voskaki, Rania; Ioannidou, Kyriaki; Boididou, Christina; Cristea, A.I.; Liakata, Maria; Kompatsiaris, Yiannis

Abstract

Sentiment lexicons and word embeddings constitute well-established sources of information for sentiment analysis in online social media. Although their effectiveness has been demonstrated in state-of-the-art sentiment analysis and related tasks in the English language, such publicly available resources are much less developed and evaluated for the Greek language. In this paper, we tackle the problems arising when analyzing text in such an under-resourced language. We present and make publicly available a rich set of such resources, ranging from a manually annotated lexicon, to semi-supervised word embedding vectors and annotated datasets for different tasks. Our experiments using different algorithms and parameters on our resources show promising results over standard baselines; on average, we achieve a 24.9% relative improvement in F-score on the cross-domain sentiment analysis task when training the same algorithms with our resources, compared to training them on more traditional feature sources, such as n-grams. Importantly, while our resources were built with the primary focus on the cross-domain sentiment analysis task, they also show promising results in related tasks, such as emotion analysis and sarcasm detection.

Citation

Tsakalidis, A., Papadopoulos, S., Voskaki, R., Ioannidou, K., Boididou, C., Cristea, A., Liakata, M., & Kompatsiaris, Y. (2018). Building and evaluating resources for sentiment analysis in the Greek language. Language Resources and Evaluation, 52(4), 1021-1044. https://doi.org/10.1007/s10579-018-9420-4

• Journal Article Type: Article
• Acceptance Date: Jul 4, 2018
• Online Publication Date: Jul 14, 2018
• Publication Date: Dec 1, 2018
• Deposit Date: Aug 2, 2018
• Publicly Available Date: Aug 2, 2018
• Journal: Language Resources and Evaluation
• Print ISSN: 1574-020X
• Electronic ISSN: 1574-0218
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 52
• Issue: 4
• Pages: 1021-1044
• DOI: https://doi.org/10.1007/s10579-018-9420-4
• Public URL: https://durham-repository.worktribe.com/output/1318507