Linguistic features for sentence difficulty prediction in ABSA
Adrian-Gabriel Chifu Sébastien Fournier
One of the challenges of natural language understanding is to deal with the
subjectivity of sentences, which may express opinions and emotions that add
layers of complexity and nuance. Sentiment analysis is a field that aims to
extract and analyze these subjective elements from text, and it can be applied
at different levels of granularity, such as document, paragraph, sentence, or
aspect. Aspect-based sentiment analysis is a well-studied topic with many
available data sets and models. However, there is no clear definition of what
makes a sentence difficult for aspect-based sentiment analysis. In this paper,
we explore this question by conducting an experiment with three data sets:
"Laptops", "Restaurants", and "MTSC" (Multi-Target-dependent Sentiment
Classification), and a merged version of these three datasets. We study the
impact of domain diversity and syntactic diversity on difficulty. We use a
combination of classifiers to identify the most difficult sentences and analyze
their characteristics. We employ two ways of defining sentence difficulty. The
first one is binary and labels a sentence as difficult if the classifiers fail
to correctly predict the sentiment polarity. The second one is a six-level
scale based on how many of the top five best-performing classifiers can
correctly predict the sentiment polarity. We also define 9 linguistic features
that, combined, aim at estimating the difficulty at sentence level.