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

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.

Acknowledgments

I would like to thank the anonymous reviewers for their constructive criticism and suggestions, which led to improvements of the paper.

Disclosure Statement

No potential conflict of interest was reported by the author.

Notes

1. The dataset is available on GitHub: https://github.com/DimitrisBil/greek-sentiment-analysis.

2. www.skroutz.gr.

3. https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1

Additional information

Funding

This research is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Programme ‘Human Resources Development, Education and Lifelong Learning’ in the context of the project “Strengthening Human Resources Research Potential via Doctorate Research (MIS-5000432), implemented by the State Scholarships Foundation (IKY).