A New Statistical Approach for Comparing Algorithms for Lexicon Based Sentiment Analysis

Lexicon based sentiment analysis usually relies on the identification of various words to which a numerical value corresponding to sentiment can be assigned. In principle, classifiers can be obtained from these algorithms by comparison with human annotation, which is considered the gold standard. In practise this is difficult in languages such as Portuguese where there is a paucity of human annotated texts. Thus in order to compare algorithms, a next best step is to directly compare different algorithms with each other without referring to human annotation. In this paper we develop methods for a statistical comparison of algorithms which does not rely on human annotation or on known class labels. We will motivate the use of marginal homogeneity tests, as well as log linear models within the framework of maximum likelihood estimation We will also show how some uncertainties present in lexicon based sentiment analysis may be similar to those which occur in human annotated tweets. We will also show how the variability in the output of different algorithms is lexicon dependent, and quantify this variability in the output within the framework of log linear models.