The Impacts of Primacy/Recency Effects on Item Review Sentiment Analysis

Primacy/recency effects, also known as serial position effects, are cognitive biases triggered when items are presented in the form of a list. Affected by these effects, users tend to recall items shown in the beginning or the end of the list more often than those in the middle. Although primacy/recency effects have been extensively analyzed within the field of psychology, they are not studied well in the context of sentiment analysis. In the literature, there are still missing studies that provide an in-depth analysis of the influences of these effects on machine learning algorithms for item review sentiment analysis. This paper bridges this gap by estimating the impacts of primacy/recency effects on sentiment analysis classifiers. We propose a primacy/recency effects-aware neural network of Bidirectional Long Short-Term Memory (so-called PriRec-BiLSTM) and compare the performance of this approach with the original neural network (BiLSTM). To sufficiently evaluate the classification accuracy of the proposed approach, we ran our approach in five datasets in different item domains, such as movies, Amazon smartphones, industry and science, and airlines Tweets. The experimental results show that considering primacy/recency effects helps increase sentiment classification accuracy.