Sentiment analysis: A combined approach

This paper combines rule-based classification, supervised learning and machine learning into a new combined method, and proposes a semi-automatic, complementary approach in which each classifier can contribute to other classifiers to achieve a good level of effectiveness.

Abstract

Sentiment analysis is an important current research area. This paper combines rule-based classification, supervised learning and machine learning into a new combined method. This method is tested on movie reviews, product reviews and MySpace comments. The results show that a hybrid classification can improve the classification effectiveness in terms of micro- and macro-averaged F1. F1 is a measure that takes both the precision and recall of a classifier’s effectiveness into account. In addition, we propose a semi-automatic, complementary approach in which each classifier can contribute to other classifiers to achieve a good level of effectiveness.