Sentiment classification of online political discussions: a comparison of a word-based and dependency-based method

Abstract

Online political discussions have received a lot of attention over the past years. In this paper we compare two sentiment lexicon approaches to classify the sentiment of sentences from political discussions. The first approach is based on applying the number of words between the target and the sentiment words to weight the sentence sentiment score. The second approach is based on using the shortest paths between target and sentiment words in a dependency graph and linguistically motivated syntactic patterns expressed as dependency paths. The methods are tested on a corpus of sentences from online Norwegian political discussions. The results show that the method based on dependency graphs performs significantly better than the word-based approach.