Sentiment analysis for the Greek language

Nikolaos Spatiotis, Iosif Mporas, Michael Paraskevas, Isidoros Perikos

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Citations (Scopus)

    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 useternet 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 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.
    Original languageEnglish
    Title of host publicationPCI 2016 - 20th Pan-Hellenic Conference on Informatics
    PublisherACM Press
    ISBN (Electronic)9781450347891
    DOIs
    Publication statusPublished - 10 Nov 2016
    Event20th Pan-Hellenic Conference on Informatics, PCI 2016 - Patra, Greece
    Duration: 10 Nov 201612 Nov 2016

    Publication series

    NameACM International Conference Proceeding Series

    Conference

    Conference20th Pan-Hellenic Conference on Informatics, PCI 2016
    Country/TerritoryGreece
    CityPatra
    Period10/11/1612/11/16

    Keywords

    • Feature vectors
    • Machine learning
    • Sentiment analysis
    • Sentiment classification
    • Text Mining

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