Tsakalidis, Adam, Papadopoulos, Symeon, Voskaki, Rania, Ioannidou, Kyriaki, Boididou, Christina, Cristea, Alexandra I., Liakata, Maria and Kompatsiaris, Yiannis (2018) Building and evaluating resources for sentiment analysis in the Greek language. Language Resources and Evaluation . doi:10.1007/s10579-018-9420-4 (In Press)

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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.

Item Type:	Journal Article
Subjects:	H Social Sciences > HM Sociology
Divisions:	Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Online social networks -- Greece, Greek language, Twitter (Firm)
Journal or Publication Title:	Language Resources and Evaluation
Publisher:	Springer
ISSN:	1574-020X
Official Date:	14 July 2018
Dates:	Date Event 14 July 2018 Available
Date of first compliant deposit:	17 September 2018
DOI:	10.1007/s10579-018-9420-4
Status:	Peer Reviewed
Publication Status:	In Press
Access rights to Published version:	Open Access
RIOXX Funder/Project Grant:	Project/Grant ID RIOXX Funder Name Funder ID EP/N510129/1 [EPSRC] Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266 EP/L016400/1 [EPSRC] Engineering and Physical Sciences Research Council http://dx.doi.org/10.13039/501100000266 UNSPECIFIED Alan Turing Institute http://dx.doi.org/10.13039/100012338

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