Examining the Impact of Feature Selection on Sentiment Analysis for the Greek Language

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.