Understanding Linguistic Variations in Neutral and Strongly Opinionated Reviews

Abstract

Reviews with a user rating close to the center of the rating scale are often referred to as neutral reviews and are prevalent in consumer feedback. By leveraging annotated data, implicit characteristics of neutral reviews can be learned for a better prediction. In case of the absence of annotated data, often, unsupervised lexicon-based approaches are employed. Nevertheless, word-level sentiment and hand-crafted aggregation rules of lexicon-based are usually inadequate for distinguishing neutral reviews. Therefore, in this study, we try to find additional distinguishing signals for identifying neutral reviews. We investi-gate a number of attributes, such as the frequency of contrasting conjunctions, extreme opinions, intensifiers, modifiers, and negation, to discover distinctive elements in neutral reviews. We find that some linguistic features, such as contrasting conjunctions and mitigators can provide additional signals that may help to distinguish neutral reviews across multi-domain datasets. Our analysis and findings deliver insights for developing effective unsupervised methods for discerning different types of reviews.