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.

摘要

情绪分析处理人们在越来越多的在线社交数据中出现的情绪，在过去几年中一直在上升。以最简单的形式，情感分析处理给定文本的极性，即其中表达的观点是正面的还是负面的。网站和社交媒体上的情绪分析或意见挖掘应用程序范围从产品评论和品牌接收到政治问题和股票市场。绝大多数情感分析研究主要处理英语数据，其中有大量现成的情感语料库和注释。除了少数值得注意的例外，缺乏对其他次要语言（如希腊语）的研究。本文处理用希腊语编写的电子产品评论的情感分析。为此，编译和使用了一个包含 480 条正面和负面评论的小型数据集，取自流行的希腊电子商务网站 www.skroutz.gr。评估了用于训练和测试数据集的不同计算模型，从具有 n-gram 特征的简单朴素贝叶斯到最先进的 BERT。对于这么小的语料库，结果看起来很有希望。