Examining the impact of feature selection on sentiment analysis for the greek language

Nikolaos Spatiotis, Michael Paraskevas, Isidoros Perikos, Iosif Mporas

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

    7 Citations (Scopus)


    Sentiment analysis identifies the attitude that a person has towards a service, a topic or an event and it is very useful for companies which receive many written opinions. Research studies have shown that the determination of sentiment in written text can be accurately determined through text and part of speech features. In this paper, we present an approach to recognize opinions in Greek language and we examine the impact of feature selection on the analysis of opinions and the performance of the classifiers. We analyze a large number of feedback and comments from teachers towards e-learning, life-long courses that have attended with the aim to specify their opinions. A number of text-based and part of speech based features from textual data are extracted and a generic approach to analyze text and determine opinion is presented. Evaluation results indicate that the approach illustrated is accurate in specifying opinions in Greek text and also sheds light on the effect that various features have on the classification performance.

    Original languageEnglish
    Title of host publicationSpeech and Computer - 19th International Conference, SPECOM 2017, Proceedings
    EditorsAlexey Karpov, Iosif Mporas, Rodmonga Potapova
    PublisherSpringer Nature
    Number of pages9
    ISBN (Print)9783319664286
    Publication statusPublished - 1 Jan 2017
    Event19th International Conference on Speech and Computer, SPECOM 2017 - Hatfield, United Kingdom
    Duration: 12 Sept 201716 Sept 2017

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10458 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference19th International Conference on Speech and Computer, SPECOM 2017
    Country/TerritoryUnited Kingdom


    • Feature selection
    • Machine learning
    • Sentiment analysis
    • Text mining


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