Although common sense and connotative knowledge come naturally to most people, computers still struggle to perform well on tasks for which such extratextual information is required. Automatic approaches to sentiment analysis and irony detection have revealed that the lack of such world knowledge undermines classification performance. In this article, we therefore address the challenge of modeling implicit or prototypical sentiment in the framework of automatic irony detection. Starting from manually annotated connoted situation phrases (e.g., “flight delays,” “sitting the whole day at the doctor’s office”), we defined the implicit sentiment held towards such situations automatically by using both a lexico-semantic knowledge base and a data-driven method. We further investigate how such implicit sentiment information affects irony detection by assessing a state-of-the-art irony classifier before and after it is informed with implicit sentiment information.

Readability research has a long and rich tradition, but there has been too little focus on general readability prediction without targeting a specific audience or text genre. Moreover, although NLP-inspired research has focused on adding more complex readability features, there is still no consensus on which features contribute most to the prediction. In this article, we investigate in close detail the feasibility of constructing a readability prediction system for English and Dutch generic text using supervised machine learning. Based on readability assessments by both experts and crowdsourcing, we implement different types of text characteristics ranging from easy-to-compute superficial text characteristics to features requiring deep linguistic processing, resulting in ten different feature groups. Both a regression and classification set-up are investigated reflecting the two possible readability prediction tasks: scoring individual texts or comparing two texts. We show that going beyond correlation calculations for readability optimization using a wrapper-based genetic algorithm optimization approach is a promising task that provides considerable insights in which feature combinations contribute to the overall readability prediction. Because we also have gold standard information available for those features requiring deep processing, we are able to investigate the true upper bound of our Dutch system. Interestingly, we will observe that the performance of our fully automatic readability prediction pipeline is on par with the pipeline using gold-standard deep syntactic and semantic information.