Sentiment analysis is a natural language processing task that has received increased attention in the last decade due to the vast amount of opinionated data on social media platforms such as Twitter. Although the methodologies employed have grown in number and sophistication, analysing irony and sarcasm still poses a severe problem. From the linguistic perspective, sarcasm has been studied in discourse analysis from several perspectives, but little attention has been given to specific metrics that measure its relevance. In this paper we describe the creation of a manually-annotated dataset where detailed text markers are included. This dataset is a sample from a larger corpus of tweets (n= 76,764) on two highly controversial films: Cats and Star Wars: The Rise of Skywalker. We took two different samples for each film, one before and one after their release, to compare reception and presence of sarcasm. We then used a sentiment analysis tool to measure the impact of sarcasm in polarity detection and then manually classified the mechanisms of sarcasm generation. The resulting corpus will be useful for machine learning approaches to sarcasm detection as well as discourse analysis studies on irony and sarcasm.
