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)

Abstract

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 Link
Pages353-361
Number of pages9
ISBN (Print)9783319664286
DOIs
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

Conference

Conference19th International Conference on Speech and Computer, SPECOM 2017
Country/TerritoryUnited Kingdom
CityHatfield
Period12/09/1716/09/17

Keywords

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

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