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Article

A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media

1
Department of Cultural Technology and Communication, University of the Aegean, 81100 Mitilini, Greece
2
Department of Informatics and Telematics, Harokopio University of Athens, 17671 Kallithea, Greece
3
Palo Services Ltd., 10562 Athens, Greece
*
Author to whom correspondence should be addressed.
Academic Editor: Willy Susilo
Information 2021, 12(8), 331; https://doi.org/10.3390/info12080331
Received: 28 July 2021 / Revised: 10 August 2021 / Accepted: 13 August 2021 / Published: 18 August 2021
(This article belongs to the Special Issue Sentiment Analysis and Affective Computing)
As the amount of content that is created on social media is constantly increasing, more and more opinions and sentiments are expressed by people in various subjects. In this respect, sentiment analysis and opinion mining techniques can be valuable for the automatic analysis of huge textual corpora (comments, reviews, tweets etc.). Despite the advances in text mining algorithms, deep learning techniques, and text representation models, the results in such tasks are very good for only a few high-density languages (e.g., English) that possess large training corpora and rich linguistic resources; nevertheless, there is still room for improvement for the other lower-density languages as well. In this direction, the current work employs various language models for representing social media texts and text classifiers in the Greek language, for detecting the polarity of opinions expressed on social media. The experimental results on a related dataset collected by the authors of the current work are promising, since various classifiers based on the language models (naive bayesian, random forests, support vector machines, logistic regression, deep feed-forward neural networks) outperform those of word or sentence-based embeddings (word2vec, GloVe), achieving a classification accuracy of more than 80%. Additionally, a new language model for Greek social media has also been trained on the aforementioned dataset, proving that language models based on domain specific corpora can improve the performance of generic language models by a margin of 2%. Finally, the resulting models are made freely available to the research community. View Full-Text
Keywords: sentiment analysis; opinion mining; Bidirectional Encoder Representations from Transformers; text embeddings; transformers; Greek social media sentiment analysis; opinion mining; Bidirectional Encoder Representations from Transformers; text embeddings; transformers; Greek social media
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MDPI and ACS Style

Alexandridis, G.; Varlamis, I.; Korovesis, K.; Caridakis, G.; Tsantilas, P. A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media. Information 2021, 12, 331. https://doi.org/10.3390/info12080331

AMA Style

Alexandridis G, Varlamis I, Korovesis K, Caridakis G, Tsantilas P. A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media. Information. 2021; 12(8):331. https://doi.org/10.3390/info12080331

Chicago/Turabian Style

Alexandridis, Georgios, Iraklis Varlamis, Konstantinos Korovesis, George Caridakis, and Panagiotis Tsantilas. 2021. "A Survey on Sentiment Analysis and Opinion Mining in Greek Social Media" Information 12, no. 8: 331. https://doi.org/10.3390/info12080331

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