Experiments in Text Classification: Analyzing the Sentiment of Electronic Product Reviews in Greek

Abstract

ABSTRACT Sentiment analysis, which deals with people’s sentiments as they appear in the growing amount of online social data, has been on the rise in the past few years. In its simplest form, sentiment analysis deals with the polarity of a given text, i.e., whether the opinion expressed in it is positive or negative. Sentiment analysis, or opinion mining applications on websites and the social media range from product reviews and brand reception to political issues and the stock market. The vast majority of the research in sentiment analysis has mostly dealt with English data, where there’s an abundance of readily available and annotated for sentiment corpora. With a few notable exceptions, the research in other minor languages such as Greek is lacking. This paper deals with sentiment analysis of electronic product reviews written in Greek. To this end, a small dataset of 480 positive and negative reviews is compiled and used, taken from the popular Greek e-commerce website, www.skroutz.gr. Different computational models for training and testing the dataset are evaluated, ranging from simple Naive Bayes with n-gram features to state-of-the-art BERT. The results look very promising for such a small corpus.