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Sentiment Analysis for the Greek Language

Authors:

Nikolaos Spatiotis,

Iosif Mporas,

Michael Paraskevas,

Isidoros PerikosAuthors Info & Claims

PCI '16: Proceedings of the 20th Pan-Hellenic Conference on Informatics

Article No.: 2, Pages 1 - 4

https://doi.org/10.1145/3003733.3003769

Published: 10 November 2016 Publication History

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Abstract

In recent years, the rapid development of technology has invaded dynamically in all areas of human activity by facilitating communication, information and interaction between people worldwide through Internet use. Internet users are no longer passive recipients of information, but new information data creators expressing ideas, opinions, feelings or their views on a service-product. Taking into account the evolution and use of mobile devices and the proliferation of wireless networks, the timely and widespread use of social networks and services satisfying the above uses are understandable. In this paper, we present an approach to analyze textual data in Greek language and extract meaningful information regarding the writer's opinion. More specifically, we present a supervised approach which classifies user generated comments into the proper polarity category. An extensive experimental study was conducted in the context of users' attitudes and opinions on e-lectures that they attended. The results were very promising, indicating that the approach was accurate and able to correctly classify opinions into the proper category.

References

[1]

B. Pang, L. Lee, Opinion mining and sentiment analysis, Found. Trends Inform. Retrieval 2 (2008) 1--135.

Digital Library

Google Scholar

[2]

B. Liu, Sentiment Analysis and Opinion Mining, Morgan and Claypool publishers, 2012 (May)

Google Scholar

[3]

Cambria, Erik (2016). "Affective computing and sentiment analysis" (PDF). IEEE Intelligent Systems 31 (2): 102--107

Digital Library

Google Scholar

[4]

Pang, Bo Lee, Lillian (2008). "4.1.2 Subjectivity Detection and Opinion Identification". Opinion Mining and Sentiment Analysis. Now Publishers Inc.

Google Scholar

[5]

Cambria, E. Schuller, B. Y.-Q. Xia, New avenues in opinion mining and sentiment analysis (extended abstract), in: Proceedings of IJCAI, Buenos Aires 2015.

Google Scholar

[6]

Pang, Bo; Lee, Lillian (2005). "Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales". Proceedings of the Association for Computational Linguistics (ACL). pp. 115--124

Digital Library

Google Scholar

[7]

Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems, 89, 14--46.

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Google Scholar

Cited By

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Liapis CKyritsis KPerikos ISpatiotis NParaskevas M(2024)A Hybrid Ensemble Approach for Greek Text Classification Based on Multilingual ModelsBig Data and Cognitive Computing10.3390/bdcc81001378:10(137)Online publication date: 14-Oct-2024
https://doi.org/10.3390/bdcc8100137
Zaikis DStylianou NVlahavas I(2023)PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource LanguagesApplied Sciences10.3390/app1305326513:5(3265)Online publication date: 3-Mar-2023
https://doi.org/10.3390/app13053265
Stefanis CGiorgi EKalentzis KTselemponis ANena ETsigalou CKontogiorgis CKourkoutas YChatzak EDokas IConstantinidis TBezirtzoglou E(2023)Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning modelsFrontiers in Public Health10.3389/fpubh.2023.119173011Online publication date: 18-Jul-2023
https://doi.org/10.3389/fpubh.2023.1191730
Show More Cited By

Sentiment Analysis for the Greek Language
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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Published In

PCI '16: Proceedings of the 20th Pan-Hellenic Conference on Informatics

November 2016

449 pages

ISBN:9781450347891

DOI:10.1145/3003733

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Greek Com Soc: Greek Computer Society
TEI: Technological Educational Institution of Athens

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 November 2016

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Short-paper
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Refereed limited

Conference

PCI '16

PCI '16: 20th Pan-Hellenic Conference on Informatics

November 10 - 12, 2016

Patras, Greece

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Overall Acceptance Rate 190 of 390 submissions, 49%

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Cited By

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Liapis CKyritsis KPerikos ISpatiotis NParaskevas M(2024)A Hybrid Ensemble Approach for Greek Text Classification Based on Multilingual ModelsBig Data and Cognitive Computing10.3390/bdcc81001378:10(137)Online publication date: 14-Oct-2024
https://doi.org/10.3390/bdcc8100137
Zaikis DStylianou NVlahavas I(2023)PIMA: Parameter-Shared Intelligent Media Analytics Framework for Low Resource LanguagesApplied Sciences10.3390/app1305326513:5(3265)Online publication date: 3-Mar-2023
https://doi.org/10.3390/app13053265
Stefanis CGiorgi EKalentzis KTselemponis ANena ETsigalou CKontogiorgis CKourkoutas YChatzak EDokas IConstantinidis TBezirtzoglou E(2023)Sentiment analysis of epidemiological surveillance reports on COVID-19 in Greece using machine learning modelsFrontiers in Public Health10.3389/fpubh.2023.119173011Online publication date: 18-Jul-2023
https://doi.org/10.3389/fpubh.2023.1191730
Zaikis DVlahavas I(2023)From Pre-Training to Meta-Learning: A Journey in Low-Resource-Language Representation LearningIEEE Access10.1109/ACCESS.2023.332633711(115951-115967)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3326337
Skoularikis KSavvas IGarani GKakarontzas G(2021)A Scalable Framework for Customer Sentiment Analysis in the Telecommunication Industry2021 29th Telecommunications Forum (TELFOR)10.1109/TELFOR52709.2021.9653423(1-4)Online publication date: 23-Nov-2021
https://doi.org/10.1109/TELFOR52709.2021.9653423
Braoudaki AKanellou EKozanitis CFatourou P(2020)Hybrid Data Driven and Rule Based Sentiment Analysis on Greek TextProcedia Computer Science10.1016/j.procs.2020.11.025178(234-243)Online publication date: 2020
https://doi.org/10.1016/j.procs.2020.11.025
Spatiotis NPerikos IMporas IParaskevas M(2019)Examining the Impact of Discretization Technique on Sentiment Analysis for the Greek Language2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)10.1109/IISA.2019.8900699(1-6)Online publication date: Jul-2019
https://doi.org/10.1109/IISA.2019.8900699
Stylianidis MGaliotou ESgouropoulou CSkourlas CVlachos VSavvas ISgouropoulou CPlagianakos VDouligeris CVoyiatzis ITampakas VSoultis G(2017)Opinion mining using an LVQ neural networkProceedings of the 21st Pan-Hellenic Conference on Informatics10.1145/3139367.3139416(1-5)Online publication date: 28-Sep-2017
https://dl.acm.org/doi/10.1145/3139367.3139416
Spatiotis NParaskevas MPerikos IMporas I(2017)Examining the Impact of Feature Selection on Sentiment Analysis for the Greek LanguageSpeech and Computer10.1007/978-3-319-66429-3_34(353-361)Online publication date: 13-Aug-2017
https://doi.org/10.1007/978-3-319-66429-3_34

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