An Empirical Study on Machine Learning-Based Sentiment Classification Using Polarity Clues

摘要

In recent years a variety of approaches in classifying the sentiment polarity of texts have been proposed. While in the majority of approaches the determination of subjectivity or polarity-related term features is at the center, the number of publicly available dictionaries is rather limited. In this paper, we investigate the performance of combining lexical resources with machine learning-based classifier for the task of sentiment classification. We systematically analyze four different English and three different German polarity dictionaries as a resources for a sentiment-based feature selection. The evaluation results show that smaller but more controlled dictionaries used for feature selection perform within a SVM-based classification setup equally good compared to the biggest available resources.