Evaluating semantic representations for extended association rules

Abstract

In this work, we evaluate the impact of changing the semantic text representation on the performance of the AR-SVS (extended association rules in semantic vector spaces) algorithm on the sentiment polarity classification task on a paper reviews dataset. To do this, we use natural language processing techniques in conjunction with machine learning classifiers. In particular, we report the classification performance using the F1 and accuracy metrics. The semantic representations that we used in our evaluation were chosen based on a systematic literature review, leading to an evaluation of AR-SVS with FastText, GloVe, and LDA2vec representations, with word2vec providing the baseline performance. The results of the experiments indicate that the choice of semantic text representation does not have major effects on the performance of AR-SVS for polarity classification. Furthermore, the results resemble those obtained in the original AR-SVS article, both in quantitative and qualitative terms. Thus, while direct improvements in classification performance were not found, we discuss other aspects and advantages of using different semantic representations.