Identifying Subjective and Figurative Language in Online Dialogue

TL;DR

This paper presents a method to automatically identify sarcastic and nasty utterances in online dialogue by adapting bootstrapping techniques, combining cue-based and pattern-based classifiers to improve detection precision.

Contribution

It extends previous monologic subjective sentence classification methods to the dialogic domain, specifically targeting sarcasm and nastiness in social media conversations.

Findings

Combined classifiers improve precision in detecting sarcastic and nasty utterances.

The adapted bootstrapping method effectively identifies domain-specific cues.

The approach is tested on unannotated online dialogue data.

Abstract

More and more of the information on the web is dialogic, from Facebook newsfeeds, to forum conversations, to comment threads on news articles. In contrast to traditional, monologic resources such as news, highly social dialogue is very frequent in social media. We aim to automatically identify sarcastic and nasty utterances in unannotated online dialogue, extending a bootstrapping method previously applied to the classification of monologic subjective sentences in Riloff and Weibe 2003. We have adapted the method to fit the sarcastic and nasty dialogic domain. Our method is as follows: 1) Explore methods for identifying sarcastic and nasty cue words and phrases in dialogues; 2) Use the learned cues to train a sarcastic (nasty) Cue-Based Classifier; 3) Learn general syntactic extraction patterns from the sarcastic (nasty) utterances and define fine-tuned sarcastic patterns to create a Pattern-Based Classifier; 4) Combine both Cue-Based and fine-tuned Pattern-Based Classifiers to maximize precision at the expense of recall and test on unannotated utterances.