Deriving Verb Predicates By Clustering Verbs with Arguments

Hand-built verb clusters such as the widely used Levin classes (Levin, 1993) have proved useful, but have limited coverage. Verb classes automatically induced from corpus data such as those from VerbKB (Wijaya, 2016), on the other hand, can give clusters with much larger coverage, and can be adapted to specific corpora such as Twitter. We present a method for clustering the outputs of VerbKB: verbs with their multiple argument types, e.g. "marry(person, person)", "feel(person, emotion)." We make use of a novel low-dimensional embedding of verbs and their arguments to produce high quality clusters in which the same verb can be in different clusters depending on its argument type. The resulting verb clusters do a better job than hand-built clusters of predicting sarcasm, sentiment, and locus of control in tweets

LT3: sentiment analysis of figurative tweets: piece of cake #NotReally

This paper describes our contribution to the SemEval-2015 Task 11 on sentiment analysis of figurative language in Twitter. We considered two approaches, classification and regression, to provide fine-grained sentiment scores for a set of tweets that are rich in sarcasm, irony and metaphor. To this end, we combined a variety of standard lexical and syntactic features with specific features for capturing figurative content. All experiments were done using supervised learning with LIBSVM. For both runs, our system ranked fourth among fifteen submissions

Sarcasm detection on Twitter

State-of-the-art approaches for sarcasm detection in social media combine lexical clues with contextual information surrounding the potentially sarcastic posting including author information. This article presents detailed methods for performing contextualizing sarcasm detection on Twitter, including data extraction, feature engineering and classification model settings. I reproduce the state-of-the-art results reported by Bamman and Smith (2015).Informatio