@inproceedings{4af04c949dcd4417a3f9244a66cc0d26,
title = "Examining the impact of feature selection on sentiment analysis for the greek language",
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.",
keywords = "Feature selection, Machine learning, Sentiment analysis, Text mining",
author = "Nikolaos Spatiotis and Michael Paraskevas and Isidoros Perikos and Iosif Mporas",
year = "2017",
month = jan,
day = "1",
doi = "10.1007/978-3-319-66429-3_34",
language = "English",
isbn = "9783319664286",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Nature ",
pages = "353--361",
editor = "Alexey Karpov and Iosif Mporas and Rodmonga Potapova",
booktitle = "Speech and Computer - 19th International Conference, SPECOM 2017, Proceedings",
address = "Netherlands",
note = "19th International Conference on Speech and Computer, SPECOM 2017 ; Conference date: 12-09-2017 Through 16-09-2017",
}