Developing a Domain-Specific Lexicon for the Greek Language

Abstract

We live in a society where a massive quantity of data is generated daily on online social network platforms. This enormous data contains vital opinion-related information that many companies and other scientific and commercial industries are trying to exploit for their benefits. For that purpose, sentiment analysis is required. Sentiment analysis or opinion mining is the branch of data analytics for extracting sentiments from messages expressed by users on a particular subject. Although, in the past years a considerable research has been made for the English language, the works of Sentiment Analysis in Greek language is not so popular, due to smaller user base. In this work, we provide a method to create domain-specific dictionaries given a corpus of tweets in the Greek language. In those Lexicons, we take into consideration the significance of each word for the specific domain, by introducing a new attribute Weightw. Also, we deploy a hybrid framework which utilizes the newly created domain-specific Lexicon with the Naïve Bayes classifier to analyze and predict the sentiment of each tweet. Our framework has the ability to merge the better of the two basic concepts, the Lexicon and Machine Learning method, and demonstrates the significance of the words for domain-specific Lexicon, for achieving optimal results when performing Sentiment Analysis.